ODI-Score.ipynb

{
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   "cell_type": "markdown",
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   "cell_type": "markdown",
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   "source": [
    "### Predict the first Innings score in a ODI\n",
    "\n",
    "1. Load the dataset from the csv file.\n",
    "2. Use “groupby” operation, to find the average number of runs, scored by each country,\n",
    "and represent it on a bar graph.\n",
    "3. Handle Missing values:\n",
    "a. If there are null values in continuous numerical column, replace the null values by\n",
    "the mean of that column\n",
    "b. If there are null values in ordinal numerical column, replace the null values by the\n",
    "mode of that column\n",
    "c. If there are null values in categorical column, replace the null values by the mode\n",
    "of that column\n",
    "d. If more than 50%the values in a column are null, then drop that entire column\n",
    "4. Remove the columns, that you think, do not contribute to the total score, in the first\n",
    "innings.\n",
    "5. Convert the categorical columns (if any), to numeric, using one hot encoding/ dummy\n",
    "encoding.\n",
    "6. Pick “total” column, as the target variable\n",
    "7. Select the relevant features.\n",
    "8. Perform train-test-split\n",
    "9. Perform Feature scaling\n",
    "10. Use\n",
    "a. Use Linear Regression\n",
    "b. Use Decision Tree Regression\n",
    "c. Use Random Forest Regression\n",
    "11. Evaluate the model\n",
    "12. Apply prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import all required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# To visualize the data\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import xticks\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# To Scale the data\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# To build the model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import xgboost as xgb\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor \n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Supress warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load the dataset from the csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mid</th>\n",
       "      <th>date</th>\n",
       "      <th>venue</th>\n",
       "      <th>bat_team</th>\n",
       "      <th>bowl_team</th>\n",
       "      <th>batsman</th>\n",
       "      <th>bowler</th>\n",
       "      <th>runs</th>\n",
       "      <th>wickets</th>\n",
       "      <th>overs</th>\n",
       "      <th>runs_last_5</th>\n",
       "      <th>wickets_last_5</th>\n",
       "      <th>striker</th>\n",
       "      <th>non-striker</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2006-06-13</td>\n",
       "      <td>Civil Service Cricket Club, Stormont</td>\n",
       "      <td>England</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>ME Trescothick</td>\n",
       "      <td>DT Johnston</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2006-06-13</td>\n",
       "      <td>Civil Service Cricket Club, Stormont</td>\n",
       "      <td>England</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>ME Trescothick</td>\n",
       "      <td>DT Johnston</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2006-06-13</td>\n",
       "      <td>Civil Service Cricket Club, Stormont</td>\n",
       "      <td>England</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>ME Trescothick</td>\n",
       "      <td>DT Johnston</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2006-06-13</td>\n",
       "      <td>Civil Service Cricket Club, Stormont</td>\n",
       "      <td>England</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>ME Trescothick</td>\n",
       "      <td>DT Johnston</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2006-06-13</td>\n",
       "      <td>Civil Service Cricket Club, Stormont</td>\n",
       "      <td>England</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>ME Trescothick</td>\n",
       "      <td>DT Johnston</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2006-06-13</td>\n",
       "      <td>Civil Service Cricket Club, Stormont</td>\n",
       "      <td>England</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>ME Trescothick</td>\n",
       "      <td>DT Johnston</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2006-06-13</td>\n",
       "      <td>Civil Service Cricket Club, Stormont</td>\n",
       "      <td>England</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>EC Joyce</td>\n",
       "      <td>D Langford-Smith</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2006-06-13</td>\n",
       "      <td>Civil Service Cricket Club, Stormont</td>\n",
       "      <td>England</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>EC Joyce</td>\n",
       "      <td>D Langford-Smith</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>2006-06-13</td>\n",
       "      <td>Civil Service Cricket Club, Stormont</td>\n",
       "      <td>England</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>EC Joyce</td>\n",
       "      <td>D Langford-Smith</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2006-06-13</td>\n",
       "      <td>Civil Service Cricket Club, Stormont</td>\n",
       "      <td>England</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>EC Joyce</td>\n",
       "      <td>D Langford-Smith</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mid        date                                 venue bat_team bowl_team  \\\n",
       "0    1  2006-06-13  Civil Service Cricket Club, Stormont  England   Ireland   \n",
       "1    1  2006-06-13  Civil Service Cricket Club, Stormont  England   Ireland   \n",
       "2    1  2006-06-13  Civil Service Cricket Club, Stormont  England   Ireland   \n",
       "3    1  2006-06-13  Civil Service Cricket Club, Stormont  England   Ireland   \n",
       "4    1  2006-06-13  Civil Service Cricket Club, Stormont  England   Ireland   \n",
       "5    1  2006-06-13  Civil Service Cricket Club, Stormont  England   Ireland   \n",
       "6    1  2006-06-13  Civil Service Cricket Club, Stormont  England   Ireland   \n",
       "7    1  2006-06-13  Civil Service Cricket Club, Stormont  England   Ireland   \n",
       "8    1  2006-06-13  Civil Service Cricket Club, Stormont  England   Ireland   \n",
       "9    1  2006-06-13  Civil Service Cricket Club, Stormont  England   Ireland   \n",
       "\n",
       "          batsman            bowler  runs  wickets  overs  runs_last_5  \\\n",
       "0  ME Trescothick       DT Johnston     0        0    0.1            0   \n",
       "1  ME Trescothick       DT Johnston     0        0    0.2            0   \n",
       "2  ME Trescothick       DT Johnston     4        0    0.3            4   \n",
       "3  ME Trescothick       DT Johnston     6        0    0.4            6   \n",
       "4  ME Trescothick       DT Johnston     6        0    0.5            6   \n",
       "5  ME Trescothick       DT Johnston     6        0    0.6            6   \n",
       "6        EC Joyce  D Langford-Smith     6        0    1.1            6   \n",
       "7        EC Joyce  D Langford-Smith     6        0    1.2            6   \n",
       "8        EC Joyce  D Langford-Smith     6        0    1.3            6   \n",
       "9        EC Joyce  D Langford-Smith     7        0    1.3            7   \n",
       "\n",
       "   wickets_last_5  striker  non-striker  total  \n",
       "0               0        0            0    301  \n",
       "1               0        0            0    301  \n",
       "2               0        0            0    301  \n",
       "3               0        0            0    301  \n",
       "4               0        0            0    301  \n",
       "5               0        0            0    301  \n",
       "6               0        0            0    301  \n",
       "7               0        0            0    301  \n",
       "8               0        0            0    301  \n",
       "9               0        0            0    301  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "odi_data =pd.read_csv(r\"C:\\Users\\sandeep kumar\\Documents\\odi.csv\")\n",
    "odi_data.head(10)"
   ]
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   "cell_type": "code",
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mid</th>\n",
       "      <th>runs</th>\n",
       "      <th>wickets</th>\n",
       "      <th>overs</th>\n",
       "      <th>runs_last_5</th>\n",
       "      <th>wickets_last_5</th>\n",
       "      <th>striker</th>\n",
       "      <th>non-striker</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>350899.000000</td>\n",
       "      <td>350899.000000</td>\n",
       "      <td>350899.000000</td>\n",
       "      <td>350899.000000</td>\n",
       "      <td>350899.000000</td>\n",
       "      <td>350899.000000</td>\n",
       "      <td>350899.000000</td>\n",
       "      <td>350899.000000</td>\n",
       "      <td>350899.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>594.360426</td>\n",
       "      <td>114.801661</td>\n",
       "      <td>2.974970</td>\n",
       "      <td>24.052899</td>\n",
       "      <td>23.548303</td>\n",
       "      <td>0.669814</td>\n",
       "      <td>35.180129</td>\n",
       "      <td>12.427944</td>\n",
       "      <td>255.355387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>343.605128</td>\n",
       "      <td>77.665959</td>\n",
       "      <td>2.298959</td>\n",
       "      <td>14.235439</td>\n",
       "      <td>11.042974</td>\n",
       "      <td>0.833895</td>\n",
       "      <td>28.115264</td>\n",
       "      <td>15.019181</td>\n",
       "      <td>62.354412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>296.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.600000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>217.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>596.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>23.600000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>257.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>893.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>36.200000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>298.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1188.000000</td>\n",
       "      <td>444.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>264.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>444.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                 mid           runs        wickets          overs  \\\n",
       "count  350899.000000  350899.000000  350899.000000  350899.000000   \n",
       "mean      594.360426     114.801661       2.974970      24.052899   \n",
       "std       343.605128      77.665959       2.298959      14.235439   \n",
       "min         1.000000       0.000000       0.000000       0.000000   \n",
       "25%       296.000000      51.000000       1.000000      11.600000   \n",
       "50%       596.000000     105.000000       3.000000      23.600000   \n",
       "75%       893.000000     168.000000       4.000000      36.200000   \n",
       "max      1188.000000     444.000000      10.000000      49.600000   \n",
       "\n",
       "         runs_last_5  wickets_last_5        striker    non-striker  \\\n",
       "count  350899.000000   350899.000000  350899.000000  350899.000000   \n",
       "mean       23.548303        0.669814      35.180129      12.427944   \n",
       "std        11.042974        0.833895      28.115264      15.019181   \n",
       "min         0.000000        0.000000       0.000000       0.000000   \n",
       "25%        17.000000        0.000000      13.000000       2.000000   \n",
       "50%        23.000000        0.000000      29.000000       7.000000   \n",
       "75%        29.000000        1.000000      50.000000      18.000000   \n",
       "max       101.000000        7.000000     264.000000     149.000000   \n",
       "\n",
       "               total  \n",
       "count  350899.000000  \n",
       "mean      255.355387  \n",
       "std        62.354412  \n",
       "min        44.000000  \n",
       "25%       217.000000  \n",
       "50%       257.000000  \n",
       "75%       298.000000  \n",
       "max       444.000000  "
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Describing the datasets\n",
    "\n",
    "odi_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 350899 entries, 0 to 350898\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   mid             350899 non-null  int64  \n",
      " 1   date            350899 non-null  object \n",
      " 2   venue           350899 non-null  object \n",
      " 3   bat_team        350899 non-null  object \n",
      " 4   bowl_team       350899 non-null  object \n",
      " 5   batsman         350899 non-null  object \n",
      " 6   bowler          350899 non-null  object \n",
      " 7   runs            350899 non-null  int64  \n",
      " 8   wickets         350899 non-null  int64  \n",
      " 9   overs           350899 non-null  float64\n",
      " 10  runs_last_5     350899 non-null  int64  \n",
      " 11  wickets_last_5  350899 non-null  int64  \n",
      " 12  striker         350899 non-null  int64  \n",
      " 13  non-striker     350899 non-null  int64  \n",
      " 14  total           350899 non-null  int64  \n",
      "dtypes: float64(1), int64(8), object(6)\n",
      "memory usage: 40.2+ MB\n"
     ]
    }
   ],
   "source": [
    "odi_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mid</th>\n",
       "      <th>date</th>\n",
       "      <th>venue</th>\n",
       "      <th>bowl_team</th>\n",
       "      <th>batsman</th>\n",
       "      <th>bowler</th>\n",
       "      <th>wickets</th>\n",
       "      <th>overs</th>\n",
       "      <th>runs_last_5</th>\n",
       "      <th>wickets_last_5</th>\n",
       "      <th>striker</th>\n",
       "      <th>non-striker</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bat_team</th>\n",
       "      <th>runs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Afghanistan</th>\n",
       "      <th>0</th>\n",
       "      <td>363</td>\n",
       "      <td>2009-04-19</td>\n",
       "      <td>Willowmoore Park</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>Noor Ali Zadran</td>\n",
       "      <td>JAR Blain</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>684</td>\n",
       "      <td>2012-03-29</td>\n",
       "      <td>Sharjah Cricket Stadium</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Karim Sadiq</td>\n",
       "      <td>T van der Gugten</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>810</td>\n",
       "      <td>2015-02-22</td>\n",
       "      <td>University Oval</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Nawroz Mangal</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>889</td>\n",
       "      <td>2014-03-01</td>\n",
       "      <td>Khan Shaheb Osman Ali Stadium</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Karim Sadiq</td>\n",
       "      <td>Rubel Hossain</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>363</td>\n",
       "      <td>2009-04-19</td>\n",
       "      <td>Willowmoore Park</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>Noor Ali Zadran</td>\n",
       "      <td>JAR Blain</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Zimbabwe</th>\n",
       "      <th>313</th>\n",
       "      <td>392</td>\n",
       "      <td>2009-08-14</td>\n",
       "      <td>Queens Sports Club</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>S Matsikenyeri</td>\n",
       "      <td>Mahbubul Alam</td>\n",
       "      <td>5</td>\n",
       "      <td>48.5</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>17</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>392</td>\n",
       "      <td>2009-08-14</td>\n",
       "      <td>Queens Sports Club</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>S Matsikenyeri</td>\n",
       "      <td>Shakib Al Hasan</td>\n",
       "      <td>5</td>\n",
       "      <td>49.6</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>23</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>392</td>\n",
       "      <td>2009-08-14</td>\n",
       "      <td>Queens Sports Club</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>E Chigumbura</td>\n",
       "      <td>Shakib Al Hasan</td>\n",
       "      <td>6</td>\n",
       "      <td>49.2</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>392</td>\n",
       "      <td>2009-08-14</td>\n",
       "      <td>Queens Sports Club</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>MN Waller</td>\n",
       "      <td>Shakib Al Hasan</td>\n",
       "      <td>6</td>\n",
       "      <td>49.3</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>392</td>\n",
       "      <td>2009-08-14</td>\n",
       "      <td>Queens Sports Club</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>E Chigumbura</td>\n",
       "      <td>Shakib Al Hasan</td>\n",
       "      <td>7</td>\n",
       "      <td>49.5</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6662 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mid        date                          venue    bowl_team  \\\n",
       "bat_team    runs                                                                \n",
       "Afghanistan 0     363  2009-04-19               Willowmoore Park     Scotland   \n",
       "            1     684  2012-03-29        Sharjah Cricket Stadium  Netherlands   \n",
       "            2     810  2015-02-22                University Oval    Sri Lanka   \n",
       "            3     889  2014-03-01  Khan Shaheb Osman Ali Stadium   Bangladesh   \n",
       "            4     363  2009-04-19               Willowmoore Park     Scotland   \n",
       "...               ...         ...                            ...          ...   \n",
       "Zimbabwe    313   392  2009-08-14             Queens Sports Club   Bangladesh   \n",
       "            319   392  2009-08-14             Queens Sports Club   Bangladesh   \n",
       "            320   392  2009-08-14             Queens Sports Club   Bangladesh   \n",
       "            322   392  2009-08-14             Queens Sports Club   Bangladesh   \n",
       "            323   392  2009-08-14             Queens Sports Club   Bangladesh   \n",
       "\n",
       "                          batsman            bowler  wickets  overs  \\\n",
       "bat_team    runs                                                      \n",
       "Afghanistan 0     Noor Ali Zadran         JAR Blain        0    0.1   \n",
       "            1         Karim Sadiq  T van der Gugten        0    0.1   \n",
       "            2       Nawroz Mangal        SL Malinga        0    2.6   \n",
       "            3         Karim Sadiq     Rubel Hossain        0    0.5   \n",
       "            4     Noor Ali Zadran         JAR Blain        0    0.2   \n",
       "...                           ...               ...      ...    ...   \n",
       "Zimbabwe    313    S Matsikenyeri     Mahbubul Alam        5   48.5   \n",
       "            319    S Matsikenyeri   Shakib Al Hasan        5   49.6   \n",
       "            320      E Chigumbura   Shakib Al Hasan        6   49.2   \n",
       "            322         MN Waller   Shakib Al Hasan        6   49.3   \n",
       "            323      E Chigumbura   Shakib Al Hasan        7   49.5   \n",
       "\n",
       "                  runs_last_5  wickets_last_5  striker  non-striker  total  \n",
       "bat_team    runs                                                            \n",
       "Afghanistan 0               0               0        0            0    295  \n",
       "            1               1               0        1            0    153  \n",
       "            2               2               0        2            0    232  \n",
       "            3               3               0        1            1    254  \n",
       "            4               4               0        4            0    295  \n",
       "...                       ...             ...      ...          ...    ...  \n",
       "Zimbabwe    313            59               1       61           17    323  \n",
       "            319            65               1       61           23    323  \n",
       "            320            65               2       61            0    323  \n",
       "            322            61               2       61            2    323  \n",
       "            323            54               3       61            0    323  \n",
       "\n",
       "[6662 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# applying groupby() function to group the data on 'date' value. \n",
    "gp = odi_data.groupby(['bat_team','runs']) \n",
    "\n",
    "# Let's print the first entries in all the groups formed. \n",
    "gp.first() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['England', 'Pakistan', 'Sri Lanka', 'Australia', 'South Africa',\n",
       "       'New Zealand', 'Bangladesh', 'West Indies', 'India', 'Zimbabwe',\n",
       "       'Ireland', 'Scotland', 'Kenya', 'Bermuda', 'Netherlands', 'Canada',\n",
       "       'Asia XI', 'Afghanistan', 'United Arab Emirates', 'Hong Kong',\n",
       "       'Papua New Guinea'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odi_data['bat_team'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total 21 countries are participating in ODI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mid               0\n",
       "date              0\n",
       "venue             0\n",
       "bat_team          0\n",
       "bowl_team         0\n",
       "batsman           0\n",
       "bowler            0\n",
       "runs              0\n",
       "wickets           0\n",
       "overs             0\n",
       "runs_last_5       0\n",
       "wickets_last_5    0\n",
       "striker           0\n",
       "non-striker       0\n",
       "total             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odi_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Means the data have not any NaN value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114.80166087677651"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_value=odi_data['runs'].mean()\n",
    "mean_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40283788"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Total_run=odi_data['runs'].sum()\n",
    "Total_run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average number of runs, scored by each country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1918275.619047619"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total 21 countries are participating in ODI.\n",
    "Avg_score=Total_run/21\n",
    "Avg_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20]),\n",
       " <a list of 21 Text xticklabel objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (15,5)\n",
    "sns.countplot(x=\"bat_team\",  data = odi_data)\n",
    "plt.xlabel('Coutry')\n",
    "plt.ylabel('frequency')\n",
    "xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "         13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "         26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "         39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "         52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "         65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "         78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "         91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "        104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "        117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "        130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "        143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
       "        156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
       "        169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "        182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
       "        195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,\n",
       "        208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,\n",
       "        221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,\n",
       "        234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,\n",
       "        247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,\n",
       "        260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,\n",
       "        273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,\n",
       "        286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,\n",
       "        299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,\n",
       "        312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324,\n",
       "        325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337,\n",
       "        338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350,\n",
       "        351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363,\n",
       "        364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376,\n",
       "        377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389,\n",
       "        390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402,\n",
       "        403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415,\n",
       "        416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428,\n",
       "        429, 430, 431, 432, 433, 434, 435, 436, 437]),\n",
       " <a list of 438 Text xticklabel objects>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (15,5)\n",
    "sns.countplot(x=\"runs\",  data = odi_data)\n",
    "plt.xlabel('Coutry')\n",
    "plt.ylabel('runs')\n",
    "plt.title('Runs scored by each country ')\n",
    "xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 350899 entries, 0 to 350898\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   mid             350899 non-null  int64  \n",
      " 1   date            350899 non-null  object \n",
      " 2   venue           350899 non-null  object \n",
      " 3   bat_team        350899 non-null  object \n",
      " 4   bowl_team       350899 non-null  object \n",
      " 5   batsman         350899 non-null  object \n",
      " 6   bowler          350899 non-null  object \n",
      " 7   runs            350899 non-null  int64  \n",
      " 8   wickets         350899 non-null  int64  \n",
      " 9   overs           350899 non-null  float64\n",
      " 10  runs_last_5     350899 non-null  int64  \n",
      " 11  wickets_last_5  350899 non-null  int64  \n",
      " 12  striker         350899 non-null  int64  \n",
      " 13  non-striker     350899 non-null  int64  \n",
      " 14  total           350899 non-null  int64  \n",
      "dtypes: float64(1), int64(8), object(6)\n",
      "memory usage: 40.2+ MB\n"
     ]
    }
   ],
   "source": [
    "odi_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mid               0\n",
       "date              0\n",
       "venue             0\n",
       "bat_team          0\n",
       "bowl_team         0\n",
       "batsman           0\n",
       "bowler            0\n",
       "runs              0\n",
       "wickets           0\n",
       "overs             0\n",
       "runs_last_5       0\n",
       "wickets_last_5    0\n",
       "striker           0\n",
       "non-striker       0\n",
       "total             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odi_data.select_dtypes(include='object').isnull().sum()>0\n",
    "odi_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total             0\n",
       "non-striker       0\n",
       "striker           0\n",
       "wickets_last_5    0\n",
       "runs_last_5       0\n",
       "overs             0\n",
       "wickets           0\n",
       "runs              0\n",
       "bowler            0\n",
       "batsman           0\n",
       "bowl_team         0\n",
       "bat_team          0\n",
       "venue             0\n",
       "date              0\n",
       "mid               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking  NAN values columnwise\n",
    "\n",
    "missing_values = (odi_data.isnull().sum()).sort_values(ascending=False)\n",
    "missing_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It Means that the data have not any NaN value.Se we don't need null values from mean ,median or others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>venue</th>\n",
       "      <th>bat_team</th>\n",
       "      <th>bowl_team</th>\n",
       "      <th>batsman</th>\n",
       "      <th>bowler</th>\n",
       "      <th>runs</th>\n",
       "      <th>wickets</th>\n",
       "      <th>overs</th>\n",
       "      <th>runs_last_5</th>\n",
       "      <th>wickets_last_5</th>\n",
       "      <th>striker</th>\n",
       "      <th>non-striker</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Civil Service Cricket Club, Stormont</td>\n",
       "      <td>England</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>ME Trescothick</td>\n",
       "      <td>DT Johnston</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Civil Service Cricket Club, Stormont</td>\n",
       "      <td>England</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>ME Trescothick</td>\n",
       "      <td>DT Johnston</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Civil Service Cricket Club, Stormont</td>\n",
       "      <td>England</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>ME Trescothick</td>\n",
       "      <td>DT Johnston</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Civil Service Cricket Club, Stormont</td>\n",
       "      <td>England</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>ME Trescothick</td>\n",
       "      <td>DT Johnston</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Civil Service Cricket Club, Stormont</td>\n",
       "      <td>England</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>ME Trescothick</td>\n",
       "      <td>DT Johnston</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  venue bat_team bowl_team         batsman  \\\n",
       "0  Civil Service Cricket Club, Stormont  England   Ireland  ME Trescothick   \n",
       "1  Civil Service Cricket Club, Stormont  England   Ireland  ME Trescothick   \n",
       "2  Civil Service Cricket Club, Stormont  England   Ireland  ME Trescothick   \n",
       "3  Civil Service Cricket Club, Stormont  England   Ireland  ME Trescothick   \n",
       "4  Civil Service Cricket Club, Stormont  England   Ireland  ME Trescothick   \n",
       "\n",
       "        bowler  runs  wickets  overs  runs_last_5  wickets_last_5  striker  \\\n",
       "0  DT Johnston     0        0    0.1            0               0        0   \n",
       "1  DT Johnston     0        0    0.2            0               0        0   \n",
       "2  DT Johnston     4        0    0.3            4               0        0   \n",
       "3  DT Johnston     6        0    0.4            6               0        0   \n",
       "4  DT Johnston     6        0    0.5            6               0        0   \n",
       "\n",
       "   non-striker  total  \n",
       "0            0    301  \n",
       "1            0    301  \n",
       "2            0    301  \n",
       "3            0    301  \n",
       "4            0    301  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove date and mid Column \n",
    "\n",
    "odi_data.drop(['date','mid'], axis=1, inplace=True)\n",
    "odi_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Civil Service Cricket Club, Stormont', 'The Rose Bowl',\n",
       "       'Trent Bridge', 'Edgbaston', \"Lord's\", 'Kennington Oval',\n",
       "       'Riverside Ground', 'Old Trafford', 'Headingley',\n",
       "       'Brisbane Cricket Ground, Woolloongabba', 'Docklands Stadium',\n",
       "       'Sydney Cricket Ground', 'Adelaide Oval',\n",
       "       'Western Australia Cricket Association Ground', 'Bellerive Oval',\n",
       "       'Jade Stadium', 'Westpac Stadium', 'Rawalpindi Cricket Stadium',\n",
       "       'Gaddafi Stadium', 'Multan Cricket Stadium', 'National Stadium',\n",
       "       'Shaheed Chandu Stadium', 'Chittagong Divisional Stadium',\n",
       "       'Narayanganj Osmani Stadium', 'Queenstown Events Centre',\n",
       "       'McLean Park', 'Eden Park', 'Feroz Shah Kotla',\n",
       "       'Nahar Singh Stadium', 'Nehru Stadium, Fatorda', 'Keenan Stadium',\n",
       "       'Maharani Usharaje Trust Cricket Ground', 'Newlands',\n",
       "       \"St George's Park\", 'Kingsmead', 'New Wanderers Stadium',\n",
       "       'R Premadasa Stadium', 'Sinhalese Sports Club Ground',\n",
       "       \"Antigua Recreation Ground, St John's\",\n",
       "       'Beausejour Stadium, Gros Islet',\n",
       "       \"Queen's Park Oval, Port of Spain\", 'Sabina Park, Kingston',\n",
       "       'Warner Park, Basseterre', 'Sheikh Zayed Stadium',\n",
       "       'Sir Vivian Richards Stadium, North Sound', 'Providence Stadium',\n",
       "       \"National Cricket Stadium, St George's\",\n",
       "       'Kensington Oval, Bridgetown', 'Harare Sports Club',\n",
       "       'SuperSport Park', 'Melbourne Cricket Ground',\n",
       "       'Gymkhana Club Ground',\n",
       "       'Punjab Cricket Association Stadium, Mohali',\n",
       "       'Sardar Patel Stadium, Motera', 'Sawai Mansingh Stadium',\n",
       "       'Brabourne Stadium', 'Seddon Park', 'Goodyear Park',\n",
       "       'Kinrara Academy Oval', 'Sedgars Park', 'Iqbal Stadium',\n",
       "       'County Ground', 'Willowmoore Park', 'Buffalo Park',\n",
       "       'Shere Bangla National Stadium', 'Ruaraka Sports Club Ground',\n",
       "       'Jaffery Sports Club Ground', 'MA Chidambaram Stadium, Chepauk',\n",
       "       'Indian Petrochemicals Corporation Limited Sports Complex Ground',\n",
       "       'Madhavrao Scindia Cricket Ground',\n",
       "       'Andhra Cricket Association-Visakhapatnam District Cricket Association Stadium',\n",
       "       'M Chinnaswamy Stadium', 'Rangiri Dambulla International Stadium',\n",
       "       'P Saravanamuttu Stadium', 'Nehru Stadium',\n",
       "       'Rajiv Gandhi International Stadium, Uppal', 'Sector 16 Stadium',\n",
       "       'Reliance Stadium', 'Vidarbha Cricket Association Ground',\n",
       "       'Wankhede Stadium', 'Green Park', 'Captain Roop Singh Stadium',\n",
       "       'Queens Sports Club', 'Arnos Vale Ground, Kingstown',\n",
       "       'Clontarf Cricket Club Ground', 'Niaz Stadium, Hyderabad',\n",
       "       'Sheikhupura Stadium', 'Marrara Cricket Ground', 'Senwes Park',\n",
       "       'OUTsurance Oval', 'Barabati Stadium', 'AMI Stadium',\n",
       "       'Dubai Sports City Cricket Stadium', 'Windsor Park, Roseau',\n",
       "       'Maple Leaf North-West Ground',\n",
       "       'Vidarbha Cricket Association Stadium, Jamtha',\n",
       "       'Zohur Ahmed Chowdhury Stadium', 'University Oval',\n",
       "       'Sophia Gardens', 'Eden Gardens',\n",
       "       'Mahinda Rajapaksa International Cricket Stadium, Sooriyawewa',\n",
       "       'Pallekele International Cricket Stadium',\n",
       "       'Zahur Ahmed Chowdhury Stadium', 'VRA Ground',\n",
       "       'Cambusdoon New Ground', 'Dubai International Cricket Stadium',\n",
       "       'Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium',\n",
       "       'Boland Park', 'De Beers Diamond Oval', 'Cobham Oval (New)',\n",
       "       'Sharjah Cricket Stadium', 'Holkar Cricket Stadium',\n",
       "       'Mombasa Sports Club Ground',\n",
       "       'Saurashtra Cricket Association Stadium',\n",
       "       'JSCA International Stadium Complex',\n",
       "       'Himachal Pradesh Cricket Association Stadium',\n",
       "       'The Village, Malahide', 'Chevrolet Park',\n",
       "       'Sharjah Cricket Association Stadium', 'Manuka Oval',\n",
       "       'Sheikh Abu Naser Stadium', 'ICC Global Cricket Academy',\n",
       "       'Grange Cricket Club Ground, Raeburn Place',\n",
       "       'Maharashtra Cricket Association Stadium', 'Hagley Oval',\n",
       "       'Saxton Oval', 'Khan Shaheb Osman Ali Stadium',\n",
       "       'Bert Sutcliffe Oval', 'Bay Oval', 'ICC Academy', 'Basin Reserve',\n",
       "       'Mission Road Ground, Mong Kok', 'Malahide',\n",
       "       'Greater Noida Sports Complex Ground',\n",
       "       'Darren Sammy National Cricket Stadium, Gros Islet',\n",
       "       'Galle International Stadium'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odi_data['venue'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 19, 121, 123,  31,  58,  53,  97,  84,  42,  13,  25, 120,   1,\n",
       "       130,   9,  50, 131,  95,  33,  73,  77, 108,  18,  75,  91,  69,\n",
       "        30,  32,  74,  79,  52,  62,  81, 118,  56,  80,  92, 115,   3,\n",
       "         8,  89,  99, 129, 112, 116,  87,  76,  54,  41, 119,  70,  39,\n",
       "        88, 100, 102,  12, 105,  35,  57, 106,  48,  22, 132,  14, 114,\n",
       "        98,  51,  60,  47,  61,   2,  59,  94,  85,  78,  93, 104,  96,\n",
       "       126, 128,  38,  16,  90,   4,  20,  82, 113,  68, 107,  83,   5,\n",
       "         0,  28, 133,  67, 127, 135, 124, 117,  29,  64,  86, 134, 125,\n",
       "        15,  27,  26,  11,  24,  21, 110,  44,  72, 101,  49,  43, 122,\n",
       "        17, 109,  66, 111,  46,  36,  63,  40, 103,  55,  10,   7,  45,\n",
       "         6,  71,  65,  37,  23,  34])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label_encoder object knows how to understand word labels. \n",
    "label_encoder = preprocessing.LabelEncoder() \n",
    "  \n",
    "# Encode labels in column 'Location'. \n",
    "odi_data['venue']= label_encoder.fit_transform(odi_data['venue']) \n",
    "odi_data['venue'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['England', 'Pakistan', 'Sri Lanka', 'Australia', 'South Africa',\n",
       "       'New Zealand', 'Bangladesh', 'West Indies', 'India', 'Zimbabwe',\n",
       "       'Ireland', 'Scotland', 'Kenya', 'Bermuda', 'Netherlands', 'Canada',\n",
       "       'Asia XI', 'Afghanistan', 'United Arab Emirates', 'Hong Kong',\n",
       "       'Papua New Guinea'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odi_data['bat_team'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6, 13, 17,  2, 16, 12,  3, 19,  8, 20,  9, 15, 10,  4, 11,  5,  1,\n",
       "        0, 18,  7, 14])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odi_data['bat_team']= label_encoder.fit_transform(odi_data['bat_team']) \n",
    "odi_data['bat_team'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Ireland', 'Pakistan', 'England', 'Sri Lanka', 'South Africa',\n",
       "       'Australia', 'New Zealand', 'India', 'West Indies', 'Bangladesh',\n",
       "       'Zimbabwe', 'Scotland', 'Bermuda', 'Netherlands', 'Canada',\n",
       "       'Kenya', 'Africa XI', 'United Arab Emirates', 'Hong Kong',\n",
       "       'Afghanistan', 'Papua New Guinea'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odi_data['bowl_team'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9, 13,  6, 17, 16,  2, 12,  8, 19,  3, 20, 15,  4, 11,  5, 10,  1,\n",
       "       18,  7,  0, 14])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odi_data['bowl_team']= label_encoder.fit_transform(odi_data['bowl_team']) \n",
    "odi_data['bowl_team'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ME Trescothick', 'EC Joyce', 'AJ Strauss', 'PD Collingwood',\n",
       "       'IR Bell', 'JWM Dalrymple', 'GO Jones', 'G Chapple', 'LE Plunkett',\n",
       "       'KP Pietersen', 'R Clarke', 'CMW Read', 'J Lewis', 'SCJ Broad',\n",
       "       'Mohammad Hafeez', 'Shahid Afridi', 'Younis Khan',\n",
       "       'Mohammad Yousuf', 'Inzamam-ul-Haq', 'Shoaib Malik',\n",
       "       'Abdul Razzaq', 'Kamran Akmal', 'Shoaib Akhtar', 'Naved-ul-Hasan',\n",
       "       'Imran Farhat', 'Iftikhar Anjum', 'Mohammad Asif', 'WU Tharanga',\n",
       "       'ST Jayasuriya', 'DPMD Jayawardene', 'KC Sangakkara', 'TM Dilshan',\n",
       "       'RP Arnold', 'CK Kapugedera', 'WPUJC Vaas', 'SL Malinga',\n",
       "       'CRD Fernando', 'M Muralitharan', 'MF Maharoof', 'HMCM Bandara',\n",
       "       'AN Cook', 'VS Solanki', 'TT Bresnan', 'AC Gilchrist',\n",
       "       'RT Ponting', 'SM Katich', 'DR Martyn', 'A Symonds', 'MJ Clarke',\n",
       "       'MEK Hussey', 'B Lee', 'NW Bracken', 'SR Clark', 'GD McGrath',\n",
       "       'PA Jaques', 'BJ Haddin', 'JR Hopes', 'BR Dorey', 'J Mubarak',\n",
       "       'MS Atapattu', 'WCA Ganegama', 'KMDN Kulasekara', 'GC Smith',\n",
       "       'HH Dippenaar', 'JJ van der Wath', 'JA Rudolph', 'HH Gibbs',\n",
       "       'SM Pollock', 'JM Kemp', 'PDRL Perera', 'DA Gunawardene',\n",
       "       'L Vincent', 'JM How', 'SP Fleming', 'PG Fulton', 'HJH Marshall',\n",
       "       'SB Styris', 'CL Cairns', 'BB McCullum', 'DL Vettori',\n",
       "       'JEC Franklin', 'SE Bond', 'Salman Butt', 'Umar Gul',\n",
       "       'Yasir Arafat', 'Mohammad Sami', 'NJ Astle', 'Rajin Saleh',\n",
       "       'Shahriar Nafees', 'Aftab Ahmed', 'Mohammad Ashraful',\n",
       "       'Habibul Bashar', 'Khaled Mashud', 'Khaled Mahmud',\n",
       "       'Mohammad Rafique', 'Alok Kapali', 'Mashrafe Mortaza',\n",
       "       'Syed Rasel', 'KHRK Fernando', 'KS Lokuarachchi', 'Tushar Imran',\n",
       "       'Abdur Razzak', 'Shahadat Hossain', 'CH Gayle', 'D Ganga',\n",
       "       'RS Morton', 'RR Sarwan', 'S Chanderpaul', 'WW Hinds', 'DR Smith',\n",
       "       'D Ramdin', 'IDR Bradshaw', 'FH Edwards', 'LRPL Taylor',\n",
       "       'KD Mills', 'G Gambhir', 'V Sehwag', 'R Dravid', 'Yuvraj Singh',\n",
       "       'M Kaif', 'SK Raina', 'IK Pathan', 'MS Dhoni', 'Harbhajan Singh',\n",
       "       'RP Singh', 'S Sreesanth', 'MJ Prior', 'OA Shah', 'A Flintoff',\n",
       "       'ID Blackwell', 'Kabir Ali', 'JM Anderson', 'RR Powar',\n",
       "       'Y Venugopal Rao', 'VRV Singh', 'MM Patel', 'SI Mahmood',\n",
       "       'AB de Villiers', 'MV Boucher', 'RJ Peterson', 'AJ Hall',\n",
       "       'SR Watson', 'JH Kallis', 'R Telemachus', 'KTGD Prasad',\n",
       "       'HP Rinke', 'T Duffin', 'CJ Chibhabha', 'BRM Taylor',\n",
       "       'E Chigumbura', 'GM Strydom', 'KM Dabengwa', 'NB Mahwire',\n",
       "       'RS Higgins', 'P Utseya', 'EC Rainsford', 'BC Lara', 'DJ Bravo',\n",
       "       'JE Taylor', 'V Sibanda', 'T Mupariwa', 'AJ Ireland', 'MN Samuels',\n",
       "       'CS Baugh', 'AB Agarkar', 'S Chattergoon', 'RV Uthappa',\n",
       "       'DBL Powell', 'CD Collymore', 'ML Hayden', 'BJ Hodge', 'GB Hogg',\n",
       "       'LPC Silva', 'WTS Porterfield', 'JP Bray', 'EJG Morgan',\n",
       "       \"NJ O'Brien\", 'AC Botha', \"KJ O'Brien\", 'AR White', 'DT Johnston',\n",
       "       'WK McCallan', 'D Langford-Smith', 'MP Vaughan', 'PA Nixon',\n",
       "       'SC Ganguly', 'SR Tendulkar', 'Z Khan', 'RS Bopara', 'F Kasteni',\n",
       "       'S Matsikenyeri', 'SC Williams', 'DF Watts', 'RM Haq', 'RR Watson',\n",
       "       'GM Hamilton', 'NFI McCallum', 'DR Brown', 'CJO Smith',\n",
       "       'JAR Blain', 'PJC Hoffmann', 'CD McMillan', 'JDP Oram',\n",
       "       'NS Poonia', 'CM Wright', 'GA Rogers', 'MA Ouma', 'RD Shah',\n",
       "       'AO Suji', 'SO Tikolo', 'T Mishra', 'CO Obuya', 'TM Odoyo',\n",
       "       'JK Kamande', 'LN Onyango', 'PJ Ongondo', 'HA Varaiya',\n",
       "       'LMP Simmons', 'Tamim Iqbal', 'Shakib Al Hasan', 'Mushfiqur Rahim',\n",
       "       'Javed Omar', 'MS Panesar', 'JF Mooney', 'WB Rankin', 'AG Prince',\n",
       "       'KED Carroll', 'DS Smith', 'A Nel', 'CK Langeveldt', 'H Masakadza',\n",
       "       'TV Mufambisi', 'LE Bosman', 'VVS Laxman', 'D Mongia',\n",
       "       'KD Karthik', 'A Kumble', 'CL White', 'MG Johnson', 'MR Gillespie',\n",
       "       'MJ Mason', 'MB Loye', 'CT Tremlett', 'KO Otieno', 'HS Modi',\n",
       "       'NN Odhiambo', 'JS Ababu', 'ML Patel', 'BJ Patel', 'T Kamungozi',\n",
       "       'Farhad Reza', 'MH Yardy', 'SJ Harmison', 'JS Patel', 'M Ntini',\n",
       "       'Imran Nazir', 'Azhar Mahmood', 'Abdur Rehman', 'JAH Marshall',\n",
       "       'AR Adams', 'AC Voges', 'AN Petersen', 'JP Duminy', 'Yasir Hameed',\n",
       "       'Faisal Iqbal', 'D Mohammed', 'AD Mascarenhas', 'PP Chawla',\n",
       "       'LJ Wright', 'KL Tucker', 'SD Outerbridge', 'DA Minors',\n",
       "       'IH Romaine', 'DL Hemp', 'JJ Tucker', 'LOB Cann', 'S Mukuddem',\n",
       "       'RD Steede', 'RDM Leverock', 'B Zuiderent', 'ES Szwarczynski',\n",
       "       'RN ten Doeschate', 'DLS van Bunge', 'AN Kervezee', 'TBM de Leede',\n",
       "       'PW Borren', 'LP van Troost', 'J Smits', 'MBS Jonkman',\n",
       "       'Mohammad Kashif', 'CJ Smith', 'H Durham', 'KAD Hurdle',\n",
       "       'SKW Kelly', 'GB Brent', 'Mehrab Hossain jnr', 'TMK Mawoyo',\n",
       "       'ML Nkala', 'CB Mpofu', 'AA Mulla', 'S Dhaniram', 'JM Davison',\n",
       "       'A Bagai', 'AM Samad', 'DR Chumney', 'DES Maxwell',\n",
       "       'GR Codrington', 'U Bhatti', 'AC Cummins', 'MO Jones',\n",
       "       'ADJ Pitcher', 'RT Lyons', 'DO Obuya', 'P Mustard', 'GP Swann',\n",
       "       'RJ Sidebottom', 'MDK Perera', 'MS Sinclair', 'MKDI Amerasinghe',\n",
       "       'RG Sharma', 'P Kumar', 'MN van Wyk', 'UDU Chandana',\n",
       "       'Abdul Jabbar', 'Mohammad Iqbal', 'TC Bastiampillai',\n",
       "       'AL Kandappah', 'A Shamshudeen', 'J Patraj', 'CK Hooper',\n",
       "       'D Soraine', 'Qaiser Ali', 'H Osinde', 'TR Ambrose', 'DR Flynn',\n",
       "       'GD Elliott', 'GJ Hopkins', 'TG Southee', 'J Botha', 'JA Morkel',\n",
       "       'DW Steyn', 'SR Patel', 'M Kartik', 'Misbah-ul-Haq',\n",
       "       'Sohail Tanvir', 'Fawad Alam', 'BA Parchment', 'DJG Sammy',\n",
       "       'R Rampaul', 'M Morkel', 'PA Browne', 'RN Lewis',\n",
       "       'Junaid Siddique', 'T Taibu', 'T Maruma', 'GH Bodi',\n",
       "       'VD Philander', 'MS Tshabalala', 'J Edness', 'JJ Celestine',\n",
       "       'RJ Trott', 'AR Pitcher', 'KC Hodsoll', 'RW Price', 'ML Udawatte',\n",
       "       'SE Marsh', 'XM Marshall', 'ADS Fletcher', 'SJ Benn', 'DJ Hussey',\n",
       "       'L Ronchi', 'Raqibul Hasan', 'Dhiman Ghosh', 'Mosharraf Hossain',\n",
       "       'Nazimuddin', 'DJ Reekers', 'TN de Grooth', 'HJC Mol',\n",
       "       'Mudassar Bukhari', 'E Schiferli', 'PM Seelaar', 'C Zhuwao',\n",
       "       'Nasir Jamshed', 'Kamran Hussain', 'Mahmudullah', 'Dolar Mahmud',\n",
       "       'Sarfraz Ahmed', 'BAW Mendis', 'K Weeraratne', 'T Thushara',\n",
       "       'YK Pathan', 'Nazmul Hossain', 'V Kohli', 'PP Ojha', 'S Badrinath',\n",
       "       'Bazid Khan', 'Naumanullah', 'Wahab Riaz', 'TD Paine',\n",
       "       'CJ Ferguson', 'NM Hauritz', 'JL Denly', 'AU Rashid', 'PM Siddle',\n",
       "       'BW Hilfenhaus', 'HM Amla', 'JL Ontong', 'J Louw', 'B Laughlin',\n",
       "       'RE van der Merwe', 'DA Warner', 'RJ Harris', 'SW Tait',\n",
       "       'ND McKenzie', 'VB van Jaarsveld', 'MJ Guptill', 'CD Cumming',\n",
       "       'NT Broom', 'KA Pollard', 'NO Miller', 'I Sharma', 'SE Findlay',\n",
       "       'BP Nash', 'Khurram Manzoor', 'Saeed Ajmal', 'SHT Kandamby',\n",
       "       'AD Mathews', 'RA Jadeja', 'A Nehra', 'SR Waters', 'AA Obanda',\n",
       "       'RR Patel', 'RL Bhudia', 'R McLaren', 'WD Parnell', 'KJ Coetzer',\n",
       "       'MM Iqbal', 'JH Stander', 'GEF Barnett', 'Rizwan Cheema',\n",
       "       'IS Billcliff', 'S Jyoti', 'Khurram Chohan', 'Noor Ali Zadran',\n",
       "       'Karim Sadiq', 'Mohammad Nabi', 'Nawroz Mangal',\n",
       "       'Asghar Stanikzai', 'Raees Ahmadzai', 'Samiullah Shenwari',\n",
       "       'Hasti Gul', 'Khaliq Dad', 'Hamid Hassan', 'Ahmed Shehzad',\n",
       "       'MJ North', 'Naeem Islam', 'Rubel Hossain', 'DM Richards',\n",
       "       'TM Dowlin', 'FL Reifer', 'DE Bernard', 'KOA Powell', 'DC Thomas',\n",
       "       'KAJ Roach', 'GC Tonge', 'TT Samaraweera', 'Umar Akmal',\n",
       "       'Mohammad Amir', 'JD Ryder', 'IG Butler', 'N Deonarine',\n",
       "       'Khalid Latif', 'MA Vermeulen', 'CK Coventry', 'AG Cremer',\n",
       "       'Enamul Haque jnr', 'MN Waller', 'CAK Walton', 'TL Best',\n",
       "       'DR Tuffey', 'RT Crandon', 'SM Davies', 'G Onions', 'AJ Redmond',\n",
       "       'MC Henriques', 'GA Manou', 'DE Bollinger', 'KM Jarvis',\n",
       "       'F Mutizwa', 'PJ Ingram', 'AJ McKay', 'Imrul Kayes',\n",
       "       'Shafiul Islam', 'SL Stewart', 'NL McCullum', 'CJ McKay',\n",
       "       'SPD Smith', 'Faisal Hossain', 'Jahurul Islam', 'C Kieswetter',\n",
       "       'IJL Trott', 'Suhrawadi Shuvo', 'Asad Shafiq', 'S Randiv',\n",
       "       'UWMBCA Welegedara', 'NLTC Perera', 'SO Ngoche', 'E Otieno',\n",
       "       'W Barresi', 'TLW Cooper', 'DM Bravo', 'CR Ervine', 'GA Lamb',\n",
       "       'F du Plessis', 'RW Chakabva', 'T Panyangara', 'PR Stirling',\n",
       "       'AR Cusack', 'GH Dockrell', 'JO Ngoche', 'BP Kruger', 'AD Russell',\n",
       "       'A Shahzad', 'KS Williamson', 'AF Buurman', 'Adeel Raja',\n",
       "       'CA Ingram', 'H Patel', 'ZE Surkari', 'AS Hansra', 'NR Kumar',\n",
       "       'K Whatham', 'HS Baidwan', 'WD Balaji Rao', 'JC Tredwell',\n",
       "       'JJ Krejza', 'LL Tsotsobe', 'R Ashwin', 'D Bishoo', 'LJ Woodcock',\n",
       "       'RAS Lakmal', 'AB Barath', 'DA Miller', 'JW Hastings',\n",
       "       'XJ Doherty', 'BMAJ Mendis', 'CR Woakes', 'PA Desai', 'PL Mommsen',\n",
       "       'JH Davey', 'RD Berrington', 'DR Lockhart', 'MA Parker',\n",
       "       'GD Drummond', 'JD Nel', 'Mohammad Shahzad', 'Javed Ahmadi',\n",
       "       'Noor-ul-Haq', 'Mirwais Ashraf', 'Shapoor Zadran', 'M Vijay',\n",
       "       'AM Blignaut', 'A Mishra', 'AB Dinda', 'UT Yadav', 'NV Ojha',\n",
       "       'Pankaj Singh', 'LD Chandimal', 'HDRL Thirimanne', 'Umar Amin',\n",
       "       'Shahzaib Hasan', 'BJ Watling', 'Zulqarnain Haider', 'J Theron',\n",
       "       'GW Flower', 'SW Masakadza', 'TJ Heggelman', 'MMA Jonkman',\n",
       "       'BP Loots', 'HMRKB Herath', 'WP Saha', 'JW Dernbach', 'PA Patel',\n",
       "       'AM Rahane', 'MK Tiwary', 'KO Meth', 'KA Edwards', 'A Martin',\n",
       "       'Taufeeq Umar', 'Usman Salahuddin', 'Mohammad Salman',\n",
       "       'Junaid Khan', 'DP Hyatt', 'S Dhawan', 'R Vinay Kumar',\n",
       "       'RMS Eranga', 'S Prasanna', 'JL Pattinson', 'MS Wade',\n",
       "       'DT Christian', 'MA Starc', 'PJ Forrest', 'SMSM Senanayake',\n",
       "       'NM Lyon', 'RJ Nicol', 'TWM Latham', 'DG Brownlie', 'AM Ellis',\n",
       "       'DAJ Bracewell', 'TS Nethula', 'C de Grandhomme', 'MD Bates',\n",
       "       'JM Bairstow', 'ST Finn', 'SG Borthwick', 'SC Meaker',\n",
       "       'Nasir Hossain', 'Shuvagata Hom', 'Adnan Akmal', 'GC Wilson',\n",
       "       'CKB Kulasekara', 'Aizaz Cheema', 'Azhar Ali', 'CR Brathwaite',\n",
       "       'SP Narine', 'GJ Bailey', 'D Elgar', 'Hammad Azam', 'J Charles',\n",
       "       'Gulbadin Naib', 'Zakiullah Zaki', 'Dawlat Zadran',\n",
       "       'Izatullah Dawlatzai', 'Amir Hamza', 'TJ Murtagh', 'TA Boult',\n",
       "       'JE Root', 'JC Buttler', 'B Kumar', 'Mohammed Shami',\n",
       "       'MC Sorensen', 'AJ Finch', 'JP Faulkner', 'NM Coulter-Nile',\n",
       "       'PJ Hughes', 'Fawad Ahmed', 'CH Morris', 'RK Kleinveldt',\n",
       "       'MDKJ Perera', 'Q de Kock', 'C Munro', 'JDS Neesham',\n",
       "       'MJ McClenaghan', 'F Behardien', 'Mohammad Irfan', 'HD Rutherford',\n",
       "       'GJ Maxwell', 'UT Khawaja', 'BCJ Cutting', 'KW Richardson',\n",
       "       'JO Holder', 'Anamul Haque', 'Mominul Haque', 'Sohag Gazi',\n",
       "       'V Permaul', 'R Gunasekera', 'Raza-ur-Rehman', 'Usman Limbada',\n",
       "       'Hamza Tariq', 'CT Mutombodzi', \"N M'shangwe\", 'Ehsan Adil',\n",
       "       'JNK Shannon', 'Ziaur Rahman', 'Robiul Islam', 'GS Ballance',\n",
       "       'BA Stokes', 'CJ Jordan', 'MJ Lumb', 'MM Ali', 'Sikandar Raza',\n",
       "       'AT Rayudu', 'TL Chatara', 'BV Vitori', 'MT Chinouya', 'Asad Ali',\n",
       "       'Haris Sohail', 'Imran Tahir', 'CJ Anderson', 'MR Marsh',\n",
       "       'CS MacLeod', 'HJW Gardiner', 'MW Machan', 'MH Cross',\n",
       "       'RML Taylor', 'I Wardlaw', 'Soumya Sarkar', 'Sabbir Rahman',\n",
       "       'Taskin Ahmed', 'Amjad Ali', 'AR Berenger', 'Krishna Chandran',\n",
       "       'Khurram Khan', 'SP Patil', 'Shaiman Anwar', 'Rohan Mustafa',\n",
       "       'Amjad Javed', 'Mohammad Naveed', 'JWA Taylor',\n",
       "       'Najibullah Zadran', 'Afsar Zazai', 'Mohammad Tauqir', 'AC Evans',\n",
       "       'RR Rossouw', 'PJ Cummins', 'AM Guruge', 'Sohaib Maqsood',\n",
       "       'Sohail Khan', 'JL Carter', 'A Balbirnie', 'Usman Ghani',\n",
       "       'Arafat Sunny', 'SR Thompson', 'PVD Chameera', 'SF Mire',\n",
       "       'FRJ Coleman', 'MA Leask', 'Nasir Aziz', 'PHT Kaushal',\n",
       "       'Rahat Ali', 'MJ Henry', 'Sharjeel Khan', 'Bilawal Bhatti',\n",
       "       'SMA Priyanjan', 'KDK Vithanage', 'Anwar Ali', 'D Daesrath',\n",
       "       'Junaid Siddiqui', 'K Kamyuka', 'JOA Gordon', 'D Murphy',\n",
       "       'SM Sharif', 'HK Bennett', 'AD Hales', 'HF Gurney',\n",
       "       'Shamsur Rahman', 'AP Devcich', 'SJ Myburgh', 'MR Swart',\n",
       "       'BN Cooper', 'IA Karim', 'RG Aga', 'AD Poynter', 'PC de Silva',\n",
       "       'STR Binny', 'Rahmat Shah', 'Al-Amin Hossain', 'AM Phangiso',\n",
       "       'KJ Abbott', 'SA Abbott', 'Raza Hasan', 'Zulfiqar Babar',\n",
       "       'J Anderson', 'SW Poynter', 'CA Young', 'GJ McCarter',\n",
       "       'R Mutumbami', 'LM Jongwe', 'J Nyumbu', 'Hashmatullah Shahidi',\n",
       "       'Shafiqullah', 'Irfan Ahmed', 'Skhawat Ali', 'JJ Atkinson',\n",
       "       'Nizakat Khan', 'Babar Hayat', 'Waqas Barkat', 'Ankur Sharma',\n",
       "       'Haseeb Amjad', 'Tanwir Afzal', 'Nadeem Ahmed', 'Ehsan Nawaz',\n",
       "       'JJ Roy', 'SW Billings', 'DJ Willey', 'MA Wood', 'MJ Santner',\n",
       "       'BM Wheeler', 'JA Burns', 'MP Stoinis', 'RJW Topley',\n",
       "       'FDM Karunaratne', 'AF Milne', 'JR Hazlewood', 'AR Patel',\n",
       "       'MM Sharma', 'Nasir Jamal', 'Sharafuddin Ashraf', 'Aftab Alam',\n",
       "       'Abul Hasan', 'PLS Gamage', 'N Dickwella', 'K Rabada', 'Liton Das',\n",
       "       'Jubair Hossain', 'Mustafizur Rahman', 'D Wiese',\n",
       "       'Mohammad Rizwan', 'Saad Nasim', 'Sami Aslam', 'TAM Siriwardana',\n",
       "       'Babar Azam', 'Yasir Shah', 'SS Pathirana', 'N Pradeep',\n",
       "       'KM Jadhav', 'MK Pandey', 'Gurkeerat Singh', 'R Dhawan',\n",
       "       'Iftikhar Ahmed', 'Zafar Gohar', 'Anshuman Rath', 'KD Shah',\n",
       "       'MS Chapman', 'MD Gunathilaka', 'MD Shanaka', 'BKG Mendis',\n",
       "       'JM Vince', 'Mohammad Nawaz (3)', 'Imad Wasim', 'Hasan Ali',\n",
       "       'LA Dawson', 'JDF Vandersay', 'HM Nicholls', 'IS Sodhi',\n",
       "       'Aamer Yamin', 'BB Chari', 'AR McBrine', 'WP Masakadza',\n",
       "       'T Muzarabani', 'Rashid Khan', 'TS Chisoro', 'TM Head',\n",
       "       'ST Gabriel', 'A Zampa', 'T Bavuma', 'SP Terry', 'BJ McCarthy',\n",
       "       'DJ Worrall', 'SM Boland', 'AL Phehlukwayo', 'CP Tremain',\n",
       "       'DS Weerakkody', 'DM de Silva', 'DAS Gunaratne', 'PADLR Sandakan',\n",
       "       'LD Madushanka', 'CBRLS Kumara', 'Yamin Ahmadzai', 'PJ Moor',\n",
       "       'N Madziva', 'MA Aponso', 'WIA Fernando', 'AK Perera',\n",
       "       'CD Wallace', 'CD de Lange', 'L Sreekumar', 'Mohammad Shahzad (2)',\n",
       "       'Muhammad Usman', 'Rameez Shahzad', 'Saqlain Haider',\n",
       "       'Fayyaz Ahmed', 'Ahmed Raza', 'CA Lynn', 'B Stanlake',\n",
       "       'PSP Handscomb', 'MRJ Watt', 'DT Tiripano', 'LH Ferguson',\n",
       "       'Mosaddek Hossain', 'Nurul Hasan', 'Tanbir Hayder', 'D Pretorius',\n",
       "       'JT Ball', 'HH Pandya', 'Fakhar Zaman', 'C Carter', 'Shahid Wasif',\n",
       "       'Ehsan Khan', 'Aizaz Khan', 'TP Ura', 'VV Morea', 'L Siaka',\n",
       "       'A Vala', 'S Bau', 'D Bau', 'MD Dai', 'H Hiri', 'JB Reva',\n",
       "       'CA Soper', 'N Vanua', 'KC Brathwaite', 'E Lewis', 'SD Hope',\n",
       "       'JN Mohammed', 'AR Nurse', 'PKD Chase', 'BM Duckett', 'J Yadav',\n",
       "       'TS Roland-Jones', 'GH Worker', 'SC Kuggeleijn',\n",
       "       'Mehedi Hasan Miraz', 'KL Rahul', 'Taijul Islam', 'CT Mumba',\n",
       "       'R Powell', 'GSNFG Jayasuriya', 'TK Musakanda', 'HG Munsey',\n",
       "       'CB Sole', 'BTJ Wheal', 'C McAuslan', 'Waqas Khan',\n",
       "       'Tanveer Ahmed', 'RP Burl', 'R Ngarava', 'Ghulam Shabber',\n",
       "       'Adnan Mufti', 'Imran Haider', 'Zahoor Khan', 'Mohammed Qasim',\n",
       "       'KA Hope', 'RL Chase', 'AS Joseph', 'KOK Williams', 'PWH de Silva',\n",
       "       'A Dananjaya'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odi_data['batsman'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([520, 242,  41, 650, 341, 420, 289, 265, 484, 459, 677, 169, 359,\n",
       "       759, 576, 816, 928, 590, 352, 827,  70, 465, 826, 631, 347, 345,\n",
       "       575, 915, 799, 221, 439, 860, 711, 163, 913, 774, 175, 500, 522,\n",
       "       317,  51, 901, 871,  23, 724, 778, 226,  11, 526, 521, 101, 622,\n",
       "       790, 278, 645, 111, 413, 124, 361, 557, 906, 454, 273, 308, 392,\n",
       "       369, 309, 779, 402, 651, 194, 479, 401, 785, 652, 312, 754, 166,\n",
       "       108, 214, 382, 762, 808, 890, 925, 586, 609, 731, 818,  78, 574,\n",
       "       322, 469, 468, 583,  85, 568, 837, 445, 460, 882,  71, 815, 152,\n",
       "       185, 721, 717, 743, 916, 227, 191, 336, 255, 495, 441, 266, 896,\n",
       "       679, 929, 497, 772, 340, 559, 326, 713, 752, 533, 639,   3, 335,\n",
       "       464, 398, 715, 922, 900, 544, 768,  19, 565, 699,  37, 795, 385,\n",
       "       686, 462, 319, 839, 155, 125, 238, 288, 451, 606, 720, 642, 243,\n",
       "       109, 207, 381, 897, 843,  38, 547, 176,  16, 744, 725, 200, 147,\n",
       "       538, 112, 272, 494, 914, 409, 244, 610,  20, 448,  63, 232, 910,\n",
       "       186, 552, 646, 755, 792, 930, 719, 251, 748, 758, 205, 706, 718,\n",
       "       287, 608, 222, 160, 371, 660, 149, 378, 619, 168, 270, 505, 691,\n",
       "        54, 784, 842, 170, 863, 393, 491, 659, 303, 490, 876, 821, 598,\n",
       "       423, 560, 383, 905,  35, 444, 229,   8, 164, 300, 873, 483, 903,\n",
       "       188, 440,   4, 167, 523, 553, 530, 510, 180, 456, 321, 616, 414,\n",
       "       540, 114, 840, 261, 525, 770, 415, 501, 349,  98,  72, 370,  56,\n",
       "        24,  53, 410, 926, 259, 187,  26, 666, 487, 450, 761, 196, 338,\n",
       "       213, 391, 492, 749, 692, 693, 103, 245, 710, 215,  52, 850, 671,\n",
       "       493, 364, 511, 579, 159, 298, 435, 773, 271, 570, 864, 539, 144,\n",
       "        14, 745, 400,   0,  50, 223, 204, 291, 883,  21, 550,  30, 723,\n",
       "       219, 641, 290, 700, 518, 561, 537, 696, 640, 549, 884,  69, 577,\n",
       "       851,  43,  10, 363, 162, 192, 675, 301, 867, 224, 277, 284, 853,\n",
       "       356, 368, 234, 791, 498, 572, 834, 263, 104, 212, 685, 499, 643,\n",
       "       709, 426, 846, 841, 280, 899, 562, 358, 388, 701,  62, 438, 727,\n",
       "       541, 764, 921,  31, 769, 208, 476, 733, 236, 595, 633, 209, 865,\n",
       "       311, 596, 241, 664, 137, 629, 466, 567, 237, 812, 106, 430, 847,\n",
       "       923, 634, 894, 667, 741, 131, 630, 917, 852, 156, 614, 395,  67,\n",
       "       665, 128, 315, 396, 360, 100, 694, 197, 697, 804, 607, 898, 527,\n",
       "       148, 620, 434, 617, 332, 763, 123, 474, 807, 767,  27, 688,   7,\n",
       "       794,  15, 714, 704, 681, 908, 447, 543, 386, 279, 737, 342, 747,\n",
       "       472, 636, 467, 580, 632,  96, 728, 810, 331, 471, 323,  81, 532,\n",
       "       624, 740, 217, 861, 256, 202, 457, 201, 436, 274, 872, 888, 573,\n",
       "       376, 337, 600, 470, 508, 161,  34, 249, 548, 143, 857, 228, 722,\n",
       "       777, 267,  40, 512, 269, 203, 453, 252, 657,  39, 351, 814, 775,\n",
       "       611, 158, 789, 258, 421, 134, 339, 836,  95, 751, 887, 612, 783,\n",
       "       240, 904, 859, 216, 173, 268, 253, 726, 845, 668,  58, 281, 407,\n",
       "       121,  29,   9, 461,  32,  74, 139, 302, 931,  64, 618, 431, 320,\n",
       "       907, 374, 389, 488, 676, 182, 486, 689,  17, 195, 418, 920, 119,\n",
       "       174, 644, 662, 384, 690, 225, 506, 276, 375, 587, 422, 637, 571,\n",
       "       823, 502,  45,   6,  18, 886, 621, 673, 481, 305, 889, 819, 115,\n",
       "       937, 365, 294, 802, 854, 546, 122, 318, 912, 417, 647,  49, 536,\n",
       "       455, 432,   5, 881, 893, 585, 425, 220, 746, 687, 708, 750, 397,\n",
       "       563, 231, 507, 655, 782, 615, 698, 874, 206,  46, 198, 869, 138,\n",
       "       514, 399, 797, 766, 757, 627, 828,  75, 275, 165,  82,  97, 172,\n",
       "       786, 283, 184, 324, 357, 296, 934, 235, 354,  86, 855, 848, 380,\n",
       "       373,  99, 592, 513,  36, 411, 613, 656, 262, 153, 703, 519, 674,\n",
       "       136, 379, 531, 250, 578, 304, 285, 885, 110, 463, 406,  89, 593,\n",
       "       831, 895, 680, 736, 892, 325, 179, 602, 246, 405, 935, 738, 292,\n",
       "       105, 157, 529, 542, 829,  66, 858, 127, 564,  94, 327, 350, 154,\n",
       "       554, 177, 313, 566, 524, 707, 333, 835, 806, 880,  87,  57, 475,\n",
       "       473, 787, 820, 739,  88, 581, 419, 625,  77, 589,  22, 716, 654,\n",
       "        47, 832, 833, 394,   1, 891,  93, 793, 670, 765, 257, 504, 626,\n",
       "       653, 729, 528, 825, 132, 781, 443,  92, 183, 427, 428, 408, 189,\n",
       "       780, 314,  25, 306, 822,  55, 771, 555, 120, 334, 695,  28, 649,\n",
       "       800, 730,  84,  48, 446, 753, 735, 936, 355, 803, 142, 286, 682,\n",
       "       489, 362, 330, 813, 353, 830, 387, 635, 130, 918,  90, 329, 879,\n",
       "       623, 248, 390, 801, 210, 509, 534, 118, 367, 551, 702, 254,  33,\n",
       "       412,  61, 545, 628, 824,  79,  73, 663, 601, 429, 496, 424, 599,\n",
       "       193, 584, 805, 809, 849, 129, 927, 796, 604, 452, 535, 297, 678,\n",
       "       344, 932,  91, 442, 558, 516, 517, 116, 403, 582, 346, 328, 480,\n",
       "       377, 316, 343,  68, 107,  59, 911, 844, 734, 868, 862, 798,  13,\n",
       "       838, 788, 113, 211, 776,  44, 171, 230, 218, 199, 648, 482, 146,\n",
       "       924, 658, 603, 503, 909,  42, 150, 151, 478, 588, 597, 732, 811,\n",
       "       264,  80, 140, 102, 669, 556, 233, 485, 594, 638, 877, 190, 416,\n",
       "       310, 260, 133, 817, 247,  83, 866, 902, 477,  12, 742, 181, 515,\n",
       "       299, 372, 141, 605, 437, 239, 760, 404,  60, 661, 117, 366, 870,\n",
       "       282, 756, 569, 449, 875, 178, 684, 293, 856, 307, 145, 126, 135,\n",
       "       919, 878, 712, 683, 295,  76, 348, 933, 591, 433, 705,  65, 458,\n",
       "       672,   2])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odi_data['batsman']= label_encoder.fit_transform(odi_data['batsman']) \n",
    "odi_data['batsman'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['DT Johnston', 'D Langford-Smith', 'JF Mooney', \"KJ O'Brien\",\n",
       "       'PJK Mooney', 'WK McCallan', 'Shoaib Akhtar', 'Mohammad Asif',\n",
       "       'Abdul Razzaq', 'Naved-ul-Hasan', 'Shahid Afridi',\n",
       "       'Mohammad Hafeez', 'Shoaib Malik', 'SCJ Broad', 'J Lewis',\n",
       "       'SI Mahmood', 'JWM Dalrymple', 'PD Collingwood', 'MH Yardy',\n",
       "       'KP Pietersen', 'SJ Harmison', 'LE Plunkett', 'TT Bresnan',\n",
       "       'WPUJC Vaas', 'SL Malinga', 'CRD Fernando', 'MF Maharoof',\n",
       "       'HMCM Bandara', 'ST Jayasuriya', 'Kabir Ali', 'VS Solanki',\n",
       "       'IR Bell', 'TM Dilshan', 'SM Pollock', 'GJP Kruger', 'AJ Hall',\n",
       "       'M Zondeki', 'JH Kallis', 'GC Smith', 'DW Steyn', 'J Botha',\n",
       "       'JJ van der Wath', 'CK Langeveldt', 'B Lee', 'NW Bracken',\n",
       "       'BR Dorey', 'JR Hopes', 'A Symonds', 'GB Hogg', 'MJ Clarke',\n",
       "       'GD McGrath', 'BJ Hodge', 'SR Clark', 'PDRL Perera',\n",
       "       'KMDN Kulasekara', 'M Muralitharan', 'KD Mills', 'SE Bond',\n",
       "       'CL Cairns', 'JDP Oram', 'DL Vettori', 'SB Styris', 'IK Pathan',\n",
       "       'S Sreesanth', 'Z Khan', 'AB Agarkar', 'SR Tendulkar', 'V Sehwag',\n",
       "       'RP Singh', 'ML Lewis', 'FH Edwards', 'IDR Bradshaw', 'JE Taylor',\n",
       "       'DR Smith', 'CH Gayle', 'RN Lewis', 'RR Powar', 'KHRK Fernando',\n",
       "       'Mashrafe Mortaza', 'Syed Rasel', 'Nazmul Hossain',\n",
       "       'Mohammad Rafique', 'Alok Kapali', 'Aftab Ahmed', 'Tapash Baisya',\n",
       "       'Manjural Islam Rana', 'MG Johnson', 'MEK Hussey', 'DJ Cullen',\n",
       "       'Shahadat Hossain', 'Abdur Razzak', 'Tushar Imran', 'Rajin Saleh',\n",
       "       'MJ Cosgrove', 'JEC Franklin', 'MJ Mason', 'NJ Astle', 'JS Patel',\n",
       "       'DK Butler', 'WW Hinds', 'RR Sarwan', 'JM Anderson', 'A Flintoff',\n",
       "       'ID Blackwell', 'Harbhajan Singh', 'Yuvraj Singh', 'G Gambhir',\n",
       "       'MJ Hoggard', 'VRV Singh', 'SR Watson', 'M Ntini', 'R Telemachus',\n",
       "       'RJ Peterson', 'JM Kemp', 'Iftikhar Anjum', 'DJ Bravo',\n",
       "       'EC Rainsford', 'NB Mahwire', 'T Mupariwa', 'CJ Chibhabha',\n",
       "       'P Utseya', 'GM Strydom', 'CD Collymore', 'D Mohammed',\n",
       "       'MN Samuels', 'KO Meth', 'HP Rinke', 'RS Higgins', 'MM Patel',\n",
       "       'Umar Gul', 'Danish Kaneria', 'PJC Hoffmann', 'JAR Blain',\n",
       "       'CM Wright', 'DR Brown', 'GA Rogers', 'RM Haq', 'KAD Hurdle',\n",
       "       'S Mukuddem', 'JJ Tucker', 'RDM Leverock', 'DCC Borden',\n",
       "       'LOB Cann', 'IH Romaine', 'CB Mpofu', 'E Chigumbura', 'GB Brent',\n",
       "       'SC Williams', 'S Matsikenyeri', 'WF Stelling', 'DJ Reekers',\n",
       "       'RN ten Doeschate', 'TBM de Leede', 'PW Borren', 'DLS van Bunge',\n",
       "       'LP van Troost', 'Shakib Al Hasan', 'MBS Jonkman', 'Adeel Raja',\n",
       "       'AC Cummins', 'U Bhatti', 'JM Davison', 'AM Samad',\n",
       "       'GR Codrington', 'S Dhaniram', 'MO Jones', 'DL Hemp', 'DBL Powell',\n",
       "       'TM Odoyo', 'PJ Ongondo', 'LN Onyango', 'JK Kamande', 'HA Varaiya',\n",
       "       'SO Tikolo', 'Mohammad Kashif', 'H Osinde', 'KT Sandher',\n",
       "       'SC Ganguly', 'RS Bopara', 'MS Panesar', 'WB Rankin', 'AC Botha',\n",
       "       'AR White', 'SW Tait', 'CD McMillan', 'MP Vaughan', 'A Nel',\n",
       "       'KA Pollard', 'MR Gillespie', 'Farhad Reza', 'D Mongia',\n",
       "       'A Kumble', 'CL White', 'LRPL Taylor', 'CT Tremlett', 'AJ Ireland',\n",
       "       'T Kamungozi', 'Yasir Arafat', 'Mohammad Sami', 'AR Adams',\n",
       "       'DR Tuffey', 'L Vincent', 'H Masakadza', 'Abdur Rehman',\n",
       "       'LMP Simmons', 'R Rampaul', 'RJ Sidebottom', 'AD Mascarenhas',\n",
       "       'PP Chawla', 'LJ Wright', 'S Thuraisingam', 'SKW Kelly',\n",
       "       'KM Dabengwa', 'Mehrab Hossain jnr', 'E Schiferli', 'NN Odhiambo',\n",
       "       'A Codrington', 'RR Emrit', 'M Morkel', 'JA Morkel', 'V Sibanda',\n",
       "       'AB de Villiers', 'GP Swann', 'OA Shah', 'MKDI Amerasinghe',\n",
       "       'CK Kapugedera', 'I Sharma', 'P Kumar', 'RG Sharma', 'RK Whelan',\n",
       "       'MJ Fourie', 'AR Cusack', 'VD Philander', 'Mahmudullah',\n",
       "       'E Otieno', 'TG Southee', 'GD Elliott', 'M Kartik',\n",
       "       'Sohail Tanvir', 'JP Duminy', 'DJG Sammy', 'S Chattergoon',\n",
       "       'CS Martin', \"IE O'Brien\", 'JD Ryder', 'GH Bodi', 'MS Tshabalala',\n",
       "       'T Maruma', 'CO Obuya', 'AS Luseno', 'RW Price', 'N Deonarine',\n",
       "       'SJ Benn', 'NO Miller', 'PL Harris', 'AN Petersen', 'JD Nel',\n",
       "       'GD Drummond', 'RD Berrington', 'PC Connell', 'Samiullah Khan',\n",
       "       'Younis Khan', 'Kamran Hussain', 'Sohail Khan', 'Fawad Alam',\n",
       "       'Abdur Rauf', 'Wahab Riaz', 'Rizwan Ahmed', 'Zahid Shah',\n",
       "       'Fahad Alhashmi', 'Amjad Javed', 'Mohammad Tauqir', 'EHSN Silva',\n",
       "       'Saqib Ali', 'Khurram Khan', 'Arshad Ali', 'Afzaal Haider',\n",
       "       'Irfan Ahmed', 'Zain Abbas', 'Nadeem Ahmed', 'Najeeb Amar',\n",
       "       'Munir Dar', 'Dolar Mahmud', 'JPR Lamsam', 'Skhawat Ali',\n",
       "       'Aman Ali', 'YK Pathan', 'MS Gony', 'PP Ojha', 'Mansoor Amjad',\n",
       "       'Saeed Ajmal', 'T Thushara', 'BAW Mendis', 'SK Raina', 'AU Rashid',\n",
       "       'NM Hauritz', 'PM Siddle', 'G Onions', 'Naeem Islam',\n",
       "       'Junaid Siddique', 'WD Parnell', 'RE van der Merwe', 'B Geeves',\n",
       "       'DJ Hussey', 'SM Harwood', 'BW Hilfenhaus', 'GJ Batty', 'SR Patel',\n",
       "       'IG Butler', 'LS Baker', 'DE Bernard', 'Mahbubul Alam',\n",
       "       'Mohammad Ashraful', 'Rubel Hossain', 'AG Cremer', 'AD Mathews',\n",
       "       'L Balaji', 'RA Jadeja', 'IJL Trott', 'RM West', 'CS MacLeod',\n",
       "       'JH Stander', 'RR Watson', 'MM Iqbal', 'DE Bollinger',\n",
       "       'B Laughlin', 'MJ North', 'KAJ Roach', 'NT Pascal',\n",
       "       'Mohammad Amir', 'SHT Kandamby', 'KTGD Prasad', 'NL McCullum',\n",
       "       'A Nehra', 'CJ McKay', 'RJ Harris', 'Enamul Haque jnr',\n",
       "       'Khurram Chohan', 'HS Baidwan', 'Rizwan Cheema', 'GC Tonge',\n",
       "       'V Kohli', 'AM Nayar', 'MS Dhoni', 'A Mishra', 'AC Voges',\n",
       "       'KM Jarvis', 'Shafiul Islam', 'AJ McKay', 'MJ Guptill',\n",
       "       'PR Stirling', 'JC Tredwell', 'A Shahzad', 'Suhrawadi Shuvo',\n",
       "       'Mohammad Irfan', 'UWMBCA Welegedara', 'RAS Lakmal', 'S Randiv',\n",
       "       'HK Bennett', 'AS Hansra', 'WD Balaji Rao', 'BRM Taylor',\n",
       "       'SO Ngoche', 'Imran Tahir', 'JJ Krejza', 'SPD Smith',\n",
       "       'GH Dockrell', 'NLTC Perera', 'HMRKB Herath', 'Mudassar Bukhari',\n",
       "       'BA Westdijk', 'BP Loots', 'TLW Cooper', 'PM Seelaar', 'LPC Silva',\n",
       "       'TG Gordon', 'F du Plessis', 'PA Desai', 'T Panyangara', 'GA Lamb',\n",
       "       'JO Ngoche', 'AD Russell', 'D Bishoo', 'KS Williamson', 'R Ashwin',\n",
       "       'M Pushpakumara', 'TT Samaraweera', 'S Tyagi', 'LL Tsotsobe',\n",
       "       'R McLaren', 'A Mithun', 'MA Starc', 'XJ Doherty', 'JW Hastings',\n",
       "       'ST Finn', 'CR Woakes', 'NG Jones', 'Shapoor Zadran',\n",
       "       'Mirwais Ashraf', 'Asghar Stanikzai', 'Mohammad Nabi',\n",
       "       'Hamid Hassan', 'Samiullah Shenwari', 'MA Parker', 'JH Davey',\n",
       "       'RT Lyons', 'AM Blignaut', 'UT Yadav', 'AB Dinda', 'BMAJ Mendis',\n",
       "       'SW Masakadza', 'GW Flower', 'J Theron', 'IA Nicolson',\n",
       "       'R Vinay Kumar', 'A van der Merwe', 'LJ Woodcock', 'JW Dernbach',\n",
       "       'Junaid Khan', 'Hammad Azam', 'A Martin', 'MK Tiwary',\n",
       "       'PJ Cummins', 'CKB Kulasekara', 'SMSM Senanayake', 'RMS Eranga',\n",
       "       'S Prasanna', 'JL Pattinson', 'DT Christian', 'BV Vitori',\n",
       "       'MN Waller', 'M de Lange', 'VR Aaron', 'SC Meaker',\n",
       "       'Nasir Hossain', 'Shuvagata Hom', 'Aizaz Cheema', 'Zahid Hussain',\n",
       "       'Junaid Siddiqui', 'H Patel', 'DAJ Bracewell', 'RJ Nicol',\n",
       "       'GW Aldridge', 'N Ncube', \"N M'shangwe\", 'DR Briggs', 'D Elgar',\n",
       "       'SP Narine', 'Elias Sunny', 'RR Patel', 'DI Allan', 'RG Aga',\n",
       "       'T van der Gugten', 'MR Swart', 'Ahsan Malik', 'Dawlat Zadran',\n",
       "       'Gulbadin Naib', 'Amir Hamza', 'Karim Sadiq', 'Javed Ahmadi',\n",
       "       'TS Nethula', 'DR Flynn', 'TA Boult', 'AM Ellis', 'Rahat Ali',\n",
       "       'I Udana', 'R Sharma', 'N Pradeep', 'B Kumar', 'JE Root',\n",
       "       'Mohammed Shami', 'MJ McClenaghan', 'C Munro', 'BA Stokes',\n",
       "       'MA Carberry', 'CJ Jordan', 'JDS Neesham', 'RK Kleinveldt',\n",
       "       'AM Phangiso', 'F Behardien', 'GJ Maxwell', 'Azhar Ali',\n",
       "       'JP Faulkner', 'JO Holder', 'BCJ Cutting', 'TL Best', 'MR Marsh',\n",
       "       'CJ Anderson', 'Abul Hasan', 'Sohag Gazi', 'V Permaul',\n",
       "       'Mominul Haque', 'NM Odhiambo', 'NM Carter', 'I Wardlaw',\n",
       "       'RML Taylor', 'MW Machan', 'Asad Ali', 'TL Chatara', 'MJG Rippon',\n",
       "       'CH Morris', 'NM Coulter-Nile', 'MM Ali', 'SD Parry', 'JD Unadkat',\n",
       "       'MM Sharma', 'AJ Finch', 'SF Mire', 'Sikandar Raza', 'Yasir Shah',\n",
       "       'Haris Sohail', 'MC Sorensen', 'AR McBrine', 'AF Milne',\n",
       "       'Aftab Alam', 'Sohaib Maqsood', 'AC Evans', 'KJ Coetzer',\n",
       "       'Taskin Ahmed', 'Sabbir Rahman', 'Mohammad Naveed', 'AM Guruge',\n",
       "       'Krishna Chandran', 'Rohan Mustafa', 'Nawroz Mangal', 'MA Leask',\n",
       "       'Kamran Shazad', 'Shaiman Anwar', 'Ehsan Adil', 'KJ Abbott',\n",
       "       'JR Hazlewood', 'FDM Karunaratne', 'Bilawal Bhatti', 'TJ Murtagh',\n",
       "       'EJ Richardson', 'AT Rayudu', 'DS Kulkarni', 'HF Gurney',\n",
       "       'SMA Priyanjan', 'Al-Amin Hossain', 'Arafat Sunny', 'AK Perera',\n",
       "       'JOA Gordon', 'Raza-ur-Rehman', 'NR Kumar', 'VJ Kingma',\n",
       "       'SM Sharif', 'ML Cummins', 'SR Thompson', 'PC de Silva',\n",
       "       'Ziaur Rahman', 'Mohammad Talha', 'Najibullah Zadran',\n",
       "       'SS Cottrell', 'CR Brathwaite', 'Zulfiqar Babar', 'Anwar Ali',\n",
       "       'KW Richardson', 'NM Lyon', 'CA Young', 'J Anderson', 'N Madziva',\n",
       "       'LM Jongwe', 'J Nyumbu', 'M Shezi', 'DT Tiripano',\n",
       "       'Mohammad Shahzad (2)', 'Fayyaz Ahmed', 'Irfan Sajid',\n",
       "       'Ahmed Raza', 'Asad Shafiq', 'MJ Henry', 'AP Devcich',\n",
       "       'Ahmed Shehzad', 'MJ Santner', 'BM Wheeler', 'DJ Willey',\n",
       "       'MA Wood', 'AC Agar', 'HDRL Thirimanne', 'GS Sandhu',\n",
       "       'MC Henriques', 'Sharafuddin Ashraf', 'Usman Ghani',\n",
       "       'Taijul Islam', 'Jubair Hossain', 'PLS Gamage', 'AR Patel',\n",
       "       'STR Binny', 'KV Sharma', 'RJW Topley', 'K Rabada', 'D Wiese',\n",
       "       'Salman Farooq', 'A Balbirnie', 'Izatullah Dawlatzai',\n",
       "       'Mustafizur Rahman', 'IS Sodhi', 'GH Worker', 'Saad Nasim',\n",
       "       'SS Pathirana', 'TAM Siriwardana', 'Imad Wasim', 'JS Paris',\n",
       "       'SM Boland', 'Gurkeerat Singh', 'R Dhawan', 'JJ Bumrah',\n",
       "       'Bilal Asif', 'Iftikhar Ahmed', 'Y Punja', 'Umair Ali',\n",
       "       'Nasir Aziz', 'Zaheer Maqsood', 'Asif Iqbal (1)', 'BJ McCarthy',\n",
       "       'KMC Bandara', 'MD Gunathilaka', 'BKG Mendis', 'PKD Chase',\n",
       "       'Hasan Ali', 'Mohammad Nawaz (3)', 'PVD Chameera', 'JDF Vandersay',\n",
       "       'A Zampa', 'Aamer Yamin', 'WP Masakadza', 'TS Chisoro',\n",
       "       'CT Mutombodzi', 'Rashid Khan', 'T Muzarabani', 'ST Gabriel',\n",
       "       'DJ Worrall', 'AL Phehlukwayo', 'CP Tremain', 'JM Mennie',\n",
       "       'TM Head', 'D Pretorius', 'T Shamsi', 'DM de Silva',\n",
       "       'PADLR Sandakan', 'DAS Gunaratne', 'CBRLS Kumara', 'LD Madushanka',\n",
       "       'Rokhan Barakzai', 'Yamin Ahmadzai', 'Rahmat Shah',\n",
       "       'Rameez Shahzad', 'CB Sole', 'CD de Lange', 'B Stanlake',\n",
       "       'LH Ferguson', 'C de Grandhomme', 'Tanwir Afzal', 'Aizaz Khan',\n",
       "       'Tanveer Ahmed', 'Anshuman Rath', 'Ehsan Khan', 'BB Sran',\n",
       "       'YS Chahal', 'Soumya Sarkar', 'Mosaddek Hossain', 'Subashis Roy',\n",
       "       'Tanbir Hayder', 'MP Stoinis', 'JT Ball', 'HH Pandya',\n",
       "       'Fahim Ashraf', 'Rumman Raees', 'Shadab Khan', 'KM Jadhav',\n",
       "       'CA Soper', 'N Vanua', 'WT Gavera', 'S Bau', 'MD Dai', 'A Vala',\n",
       "       'H Hiri', 'K Christie', 'AR Nurse', 'JN Mohammed', 'AS Joseph',\n",
       "       'JL Carter', 'KC Brathwaite', 'Mosharraf Hossain', 'KA Maharaj',\n",
       "       'SHA Rance', 'Sunzamul Islam', 'Simi Singh', 'J Mulder',\n",
       "       'Fareed Ahmad', 'Naveen-ul-Haq', 'GSNFG Jayasuriya', 'MD Shanaka',\n",
       "       'Ehsan Nawaz', 'Nizakat Khan', 'Qadeer Ahmed', 'Imran Haider',\n",
       "       'Zahoor Khan', 'R Ngarava', 'Mehedi Hasan Miraz', 'A Nao',\n",
       "       'JB Reva', 'RP Burl', 'RL Chase', 'R Powell', 'KOK Williams',\n",
       "       'Kuldeep Yadav', 'PWH de Silva'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odi_data['bowler'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([168, 137, 279, 325, 491, 686, 621, 428,  54, 474, 616, 429, 622,\n",
       "       571, 262, 577, 308, 486, 385, 333, 579, 350, 658, 688, 582, 130,\n",
       "       383, 230, 602, 339, 681, 250, 653, 585, 211,  29, 369, 281, 201,\n",
       "       170, 261, 286, 121,  85, 467,  98, 302,  11, 200, 386, 205,  92,\n",
       "       593, 487, 330, 365, 321, 573, 123, 274, 158, 567, 249, 564, 701,\n",
       "        15, 595, 675, 535, 400, 188, 244, 277, 164, 114, 532, 538, 322,\n",
       "       422, 635, 477, 435,  69,  60, 664, 420, 384, 382, 150, 615,  56,\n",
       "       666, 547, 387, 278, 392, 455, 304, 156, 690, 539, 290,   2, 243,\n",
       "       236, 700, 195, 391, 680, 597, 366, 511, 522, 292, 253, 149, 177,\n",
       "       453, 638, 117, 481, 212, 111, 138, 405, 331, 232, 542, 403, 671,\n",
       "       171, 490, 267, 125, 162, 198, 528, 318, 561, 285, 516, 145, 358,\n",
       "       247, 108, 174, 199, 570, 560, 685, 152, 533, 646, 501, 159, 359,\n",
       "       618, 376,  59,  20, 667, 291,  39, 214, 559, 407, 157, 144, 655,\n",
       "       489, 357, 287, 225, 590, 431, 223, 335, 568, 541, 415, 682,  19,\n",
       "        47, 605, 112, 409,   9, 317, 410, 192, 139,   3, 124, 361, 134,\n",
       "        30, 636, 697, 436,  42, 165, 348, 222,  57, 356, 509, 523,  23,\n",
       "       497, 353, 565, 581, 326, 424, 176, 463,   1, 536, 364, 266, 676,\n",
       "        17, 213, 479, 398, 120, 239, 480, 519, 526, 388,  43, 677, 419,\n",
       "       175, 648, 204, 363, 630, 298, 155, 558, 132, 245, 271, 206, 416,\n",
       "       637, 126,  50, 544, 447, 578, 464, 493,  40, 270, 203, 515, 484,\n",
       "       610, 699, 340, 629, 193,  55, 691, 551, 704, 189,  72, 439, 178,\n",
       "       612, 344,  76,  62, 257, 706, 468, 470, 445, 173, 300, 626,  70,\n",
       "       694, 414, 498, 421, 608, 642,  89, 580,  52, 460, 496, 196, 469,\n",
       "       312, 684, 517,  82, 151, 584, 101, 209, 594, 246, 362, 146, 418,\n",
       "       427, 555,  27,  24, 347, 513, 248, 529, 131, 282, 540, 402, 147,\n",
       "        84, 394, 319, 466, 426, 576, 336, 456,   8, 119, 520, 184, 343,\n",
       "       233, 552, 202, 673,  37, 413,   5,  22, 328, 614,  31, 389, 499,\n",
       "       269,  10, 633, 430, 669, 514, 563, 229,  48, 683,  99, 589, 256,\n",
       "       284, 592, 207, 457, 231, 444,  88,  97, 652, 495, 360, 647, 186,\n",
       "       482, 640, 197, 296,  25, 135, 334, 504, 367, 659, 566, 354, 506,\n",
       "         6, 374, 692, 307, 600, 129, 454, 619, 425,  79, 432, 234, 611,\n",
       "       373, 280, 543,  34, 668,  16,  96, 604, 218, 265, 242, 512,  14,\n",
       "       352, 306, 311, 235,   4, 397, 488, 122, 588, 531, 562, 289, 167,\n",
       "       100, 406, 370, 679, 569, 473, 623,  66, 703, 313, 224, 142, 521,\n",
       "       217, 450, 448, 161, 136, 591, 183, 537, 148, 518, 643, 412,  65,\n",
       "       172, 219,  71, 342, 309, 657, 163, 644,  35, 545, 240, 510, 451,\n",
       "        83, 276, 440, 393, 104,  87, 371, 118, 275, 525,  38, 185, 210,\n",
       "        81, 299, 295,  91, 650, 411, 116,  58, 627, 674, 441, 462, 458,\n",
       "       241, 530, 417,  77, 651, 396, 115, 459, 401, 572, 272, 404,  28,\n",
       "       574, 624, 698, 237, 378,  44,  26,  61, 628,  21, 324, 665, 607,\n",
       "       433,  36, 345, 553, 476, 372, 341, 617, 180, 323, 301, 187, 103,\n",
       "       649, 179,  51, 166, 227, 587,  68,  75,  32, 297, 550, 465, 678,\n",
       "       586, 399, 596, 485, 707, 438, 471, 598, 128, 708,  74, 338, 461,\n",
       "       107, 260, 449, 355, 264, 368, 169, 437, 194, 258,  63,  78, 390,\n",
       "        41,  64, 395,  95, 153, 375,  18, 226, 215, 377, 620, 672, 660,\n",
       "       310, 494,  46, 603, 337, 524, 315, 141, 609,   0, 259, 446, 251,\n",
       "       208, 606, 599, 645, 254, 303, 583, 220, 505, 283, 102, 252, 693,\n",
       "       670, 472, 702,  80,  93, 329, 380,  94, 492, 238, 434, 500, 273,\n",
       "        13,  53, 687, 656, 133, 549, 639, 601, 154,  33, 127, 293, 654,\n",
       "       140, 641, 160, 483, 143, 110, 349, 554, 696, 546, 548, 109, 113,\n",
       "        86, 351, 105, 663,  67, 662,  73, 181,  90, 695, 631, 442, 632,\n",
       "       661, 408, 305, 228, 190, 556, 613, 327, 106, 452, 689, 557, 379,\n",
       "        12, 221, 314,  45, 294,  49, 288, 320, 443, 316, 575, 634, 625,\n",
       "       263, 191, 475, 216, 381, 182, 478, 503, 255, 705, 507, 423,   7,\n",
       "       268, 534, 527, 508, 332, 346, 502])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odi_data['bowler']= label_encoder.fit_transform(odi_data['bowler']) \n",
    "odi_data['bowler'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>venue</th>\n",
       "      <th>bat_team</th>\n",
       "      <th>bowl_team</th>\n",
       "      <th>batsman</th>\n",
       "      <th>bowler</th>\n",
       "      <th>runs</th>\n",
       "      <th>wickets</th>\n",
       "      <th>overs</th>\n",
       "      <th>runs_last_5</th>\n",
       "      <th>wickets_last_5</th>\n",
       "      <th>striker</th>\n",
       "      <th>non-striker</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>520</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>520</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>520</td>\n",
       "      <td>168</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>520</td>\n",
       "      <td>168</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>520</td>\n",
       "      <td>168</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   venue  bat_team  bowl_team  batsman  bowler  runs  wickets  overs  \\\n",
       "0     19         6          9      520     168     0        0    0.1   \n",
       "1     19         6          9      520     168     0        0    0.2   \n",
       "2     19         6          9      520     168     4        0    0.3   \n",
       "3     19         6          9      520     168     6        0    0.4   \n",
       "4     19         6          9      520     168     6        0    0.5   \n",
       "\n",
       "   runs_last_5  wickets_last_5  striker  non-striker  total  \n",
       "0            0               0        0            0    301  \n",
       "1            0               0        0            0    301  \n",
       "2            4               0        0            0    301  \n",
       "3            6               0        0            0    301  \n",
       "4            6               0        0            0    301  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odi_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([301, 271, 235, 154, 257, 319, 261, 318, 321, 228, 245, 281, 344,\n",
       "       309, 218, 233, 282, 263, 221, 255, 224, 274, 265, 368, 288, 266,\n",
       "       161, 286, 118, 212, 195, 250, 124, 200, 276, 324, 203, 226, 294,\n",
       "       223, 289, 254, 246, 434, 130, 151, 242, 152, 198, 217, 197, 269,\n",
       "       241, 334, 353, 209, 191, 358, 279, 413, 202, 186, 331, 136, 363,\n",
       "       377, 177, 322, 104, 303, 174, 251, 247, 356, 143, 219,  91, 193,\n",
       "       243,  77, 230, 348, 300, 149, 248, 343, 290, 155, 110, 292, 205,\n",
       "       210, 270, 252, 168, 184, 302,  85, 285, 231,  80, 125, 253, 234,\n",
       "       165, 169, 213, 272, 249, 240, 258, 392, 351, 107, 153, 262, 148,\n",
       "       336, 346, 201, 162, 418, 238, 225, 278, 329, 316, 187, 178, 220,\n",
       "       146, 194, 268, 341, 259, 284, 317, 337, 211, 159, 236, 179, 239,\n",
       "       173, 137, 196, 189, 307, 182, 275,  83, 296, 306, 291, 175,  93,\n",
       "       340, 206, 323, 183, 167, 232, 273, 117, 347, 244, 181, 357, 374,\n",
       "       299, 283, 332, 308, 115, 142, 237, 330, 222, 315, 260, 176, 131,\n",
       "       264, 172, 387, 128, 127,  67, 171, 339, 188, 147, 119, 160, 328,\n",
       "       256, 320, 354, 295, 207, 216, 170, 267, 277, 312, 113, 133, 214,\n",
       "       129, 229, 350,  44, 287, 414, 370,  69, 338, 298, 327,  58, 112,\n",
       "       141, 401, 365, 333, 120, 199, 385, 192, 103, 399, 190, 293, 311,\n",
       "       139, 361, 208, 132, 280, 372, 373,  61, 304, 204, 163, 314, 138,\n",
       "       325, 227, 359, 185, 305,  74,  70, 144, 383, 342, 123, 310, 408,\n",
       "       102, 411, 417, 376, 393, 326, 156, 180, 439, 122, 215, 157, 364,\n",
       "       349, 360, 369, 297, 404, 375, 355, 438, 444, 116, 371, 367, 384,\n",
       "       378, 126, 381, 135], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odi_data['total'].unique() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing Test and Train Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=odi_data.drop('total',axis=1)\n",
    "y=odi_data.total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>venue</th>\n",
       "      <th>bat_team</th>\n",
       "      <th>bowl_team</th>\n",
       "      <th>batsman</th>\n",
       "      <th>bowler</th>\n",
       "      <th>runs</th>\n",
       "      <th>wickets</th>\n",
       "      <th>overs</th>\n",
       "      <th>runs_last_5</th>\n",
       "      <th>wickets_last_5</th>\n",
       "      <th>striker</th>\n",
       "      <th>non-striker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>105561</th>\n",
       "      <td>119</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>315</td>\n",
       "      <td>658</td>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>9.4</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29203</th>\n",
       "      <td>54</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>552</td>\n",
       "      <td>585</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33086</th>\n",
       "      <td>120</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>785</td>\n",
       "      <td>205</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>12.4</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317337</th>\n",
       "      <td>81</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>674</td>\n",
       "      <td>483</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>11.3</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302025</th>\n",
       "      <td>44</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>746</td>\n",
       "      <td>364</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        venue  bat_team  bowl_team  batsman  bowler  runs  wickets  overs  \\\n",
       "105561    119        16          6      315     658    44        2    9.4   \n",
       "29203      54         6         16      552     585     1        0    2.4   \n",
       "33086     120        12          2      785     205    37        2   12.4   \n",
       "317337     81        16         17      674     483    72        1   11.3   \n",
       "302025     44         8         16      746     364    23        1    6.5   \n",
       "\n",
       "        runs_last_5  wickets_last_5  striker  non-striker  \n",
       "105561           18               2       23            4  \n",
       "29203             1               0        1            0  \n",
       "33086            22               0       17           11  \n",
       "317337           35               0       47           24  \n",
       "302025           20               0       13            5  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(280719, 12)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70180, 12)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>venue</th>\n",
       "      <th>bat_team</th>\n",
       "      <th>bowl_team</th>\n",
       "      <th>batsman</th>\n",
       "      <th>bowler</th>\n",
       "      <th>runs</th>\n",
       "      <th>wickets</th>\n",
       "      <th>overs</th>\n",
       "      <th>runs_last_5</th>\n",
       "      <th>wickets_last_5</th>\n",
       "      <th>striker</th>\n",
       "      <th>non-striker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>165626</th>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>854</td>\n",
       "      <td>325</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326566</th>\n",
       "      <td>103</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>876</td>\n",
       "      <td>304</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110090</th>\n",
       "      <td>94</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>816</td>\n",
       "      <td>365</td>\n",
       "      <td>87</td>\n",
       "      <td>7</td>\n",
       "      <td>27.1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39941</th>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>721</td>\n",
       "      <td>597</td>\n",
       "      <td>74</td>\n",
       "      <td>4</td>\n",
       "      <td>19.6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166546</th>\n",
       "      <td>102</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>527</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>6.4</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        venue  bat_team  bowl_team  batsman  bowler  runs  wickets  overs  \\\n",
       "165626     20        11          9      854     325     8        1    2.6   \n",
       "326566    103         3         12      876     304    86        0   18.2   \n",
       "110090     94        13         17      816     365    87        7   27.1   \n",
       "39941      12        19          2      721     597    74        4   19.6   \n",
       "166546    102        12          8      527       8    23        1    6.4   \n",
       "\n",
       "        runs_last_5  wickets_last_5  striker  non-striker  \n",
       "165626            8               1        1            0  \n",
       "326566           18               0       41           35  \n",
       "110090           20               3        7            2  \n",
       "39941            11               0       14            3  \n",
       "166546           11               1        9            8  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['venue', 'bat_team', 'bowl_team', 'batsman', 'bowler', 'runs',\n",
       "       'wickets', 'overs', 'runs_last_5', 'wickets_last_5', 'striker',\n",
       "       'non-striker'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# storing column names in cols\n",
    "# scaling (the dataframe is converted to a numpy array)\n",
    "\n",
    "cols = X.columns\n",
    "cols = X.columns\n",
    "X = pd.DataFrame(scale(X))\n",
    "X.columns = cols\n",
    "X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LINEAR REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5333524291384211"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model = xgb.XGBRegressor(n_estimators=1000,learning_rate = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.1, max_delta_step=0, max_depth=6,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=1000, n_jobs=0, num_parallel_tree=1,\n",
       "             objective='reg:squarederror', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "             validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_model.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions1 = my_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9316691940387186"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test,predictions1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = odi_data['venue'].values\n",
    "y = odi_data['total'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
       "                      max_features=None, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                      random_state=None, splitter='best')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Decision Tree Regression to the odi_data.\n",
    "regressor = DecisionTreeRegressor()\n",
    "regressor.fit(X_train.reshape(-1,1), y_train.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([232.82432815, 290.47942843, 276.9575299 , ..., 246.46536412,\n",
       "       235.04945968, 239.27893309])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = regressor.predict(X_test.reshape(-1,1))\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Real Values</th>\n",
       "      <th>Predicted Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>260</td>\n",
       "      <td>232.824328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>269</td>\n",
       "      <td>290.479428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>240</td>\n",
       "      <td>276.957530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>221</td>\n",
       "      <td>272.101258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>234</td>\n",
       "      <td>234.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17540</th>\n",
       "      <td>230</td>\n",
       "      <td>254.557811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17541</th>\n",
       "      <td>312</td>\n",
       "      <td>238.904274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17542</th>\n",
       "      <td>306</td>\n",
       "      <td>246.465364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17543</th>\n",
       "      <td>206</td>\n",
       "      <td>235.049460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17544</th>\n",
       "      <td>189</td>\n",
       "      <td>239.278933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17545 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Real Values  Predicted Values\n",
       "0              260        232.824328\n",
       "1              269        290.479428\n",
       "2              240        276.957530\n",
       "3              221        272.101258\n",
       "4              234        234.000000\n",
       "...            ...               ...\n",
       "17540          230        254.557811\n",
       "17541          312        238.904274\n",
       "17542          306        246.465364\n",
       "17543          206        235.049460\n",
       "17544          189        239.278933\n",
       "\n",
       "[17545 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Real Values':y_test.reshape(-1), 'Predicted Values':y_pred.reshape(-1)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Decision Tree Regression Results \n",
    "X_grid = np.arange(min(X), max(X), 0.01)\n",
    "X_grid = X_grid.reshape((len(X_grid), 1))\n",
    "plt.rcParams['figure.figsize'] = (15,8)\n",
    "plt.scatter(X_test, y_test, color = 'red')\n",
    "plt.scatter(X_test, y_pred, color = 'green')\n",
    "plt.title('Decision Tree Regression')\n",
    "plt.xlabel('venue')\n",
    "plt.ylabel('total')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (15,8)\n",
    "plt.plot(X_grid, regressor.predict(X_grid), color = 'black')\n",
    "plt.title('Decision Tree Regression')\n",
    "plt.xlabel('venue')\n",
    "plt.ylabel('total')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([280.])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prediction\n",
    "y_pred=regressor.predict([[6.5]])\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create regressor object \n",
    "regressor = RandomForestRegressor(n_estimators = 100, random_state = 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                      random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X.reshape(-1,1), y.reshape(-1,1))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_grid = np.arange(min(X), max(X), 0.01)                      \n",
    "X_grid = X_grid.reshape((len(X_grid), 1)) \n",
    "  \n",
    "# Scatter plot for original data\n",
    "plt.rcParams['figure.figsize'] = (15,5)\n",
    "plt.scatter(X, y, color = 'red')   \n",
    "  \n",
    "# plot predicted data \n",
    "plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')  \n",
    "plt.title('Random Forest Regression') \n",
    "plt.xlabel('venue') \n",
    "plt.ylabel('total') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([280.])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=regressor.predict([[6.5]])\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see Prediction value for Decision Tree Regression and Random Forest Regression are same which value is array( [ 280. ] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}