IPL Score Prediction.ipynb

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   "outputs": [],
   "source": [
    "#importing the necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1703771963866,
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     },
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    },
    "id": "I8ZnJjVmtn8H"
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"ipl.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    "executionInfo": {
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    {
     "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>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>2008-04-18</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>SC Ganguly</td>\n",
       "      <td>P Kumar</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>P Kumar</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>P Kumar</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>P Kumar</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>P Kumar</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mid        date                  venue               bat_team  \\\n",
       "0    1  2008-04-18  M Chinnaswamy Stadium  Kolkata Knight Riders   \n",
       "1    1  2008-04-18  M Chinnaswamy Stadium  Kolkata Knight Riders   \n",
       "2    1  2008-04-18  M Chinnaswamy Stadium  Kolkata Knight Riders   \n",
       "3    1  2008-04-18  M Chinnaswamy Stadium  Kolkata Knight Riders   \n",
       "4    1  2008-04-18  M Chinnaswamy Stadium  Kolkata Knight Riders   \n",
       "\n",
       "                     bowl_team      batsman   bowler  runs  wickets  overs  \\\n",
       "0  Royal Challengers Bangalore   SC Ganguly  P Kumar     1        0    0.1   \n",
       "1  Royal Challengers Bangalore  BB McCullum  P Kumar     1        0    0.2   \n",
       "2  Royal Challengers Bangalore  BB McCullum  P Kumar     2        0    0.2   \n",
       "3  Royal Challengers Bangalore  BB McCullum  P Kumar     2        0    0.3   \n",
       "4  Royal Challengers Bangalore  BB McCullum  P Kumar     2        0    0.4   \n",
       "\n",
       "   runs_last_5  wickets_last_5  striker  non-striker  total  \n",
       "0            1               0        0            0    222  \n",
       "1            1               0        0            0    222  \n",
       "2            2               0        0            0    222  \n",
       "3            2               0        0            0    222  \n",
       "4            2               0        0            0    222  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
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   "execution_count": 4,
   "metadata": {
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    },
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    "outputId": "0f29e697-3ec1-4aa8-de17-e6d930aece46"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mid', 'date', 'venue', 'bat_team', 'bowl_team', 'batsman', 'bowler',\n",
       "       'runs', 'wickets', 'overs', 'runs_last_5', 'wickets_last_5', 'striker',\n",
       "       'non-striker', 'total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
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    },
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    "outputId": "dfd5a6dc-e2d4-4388-8465-572d254c7de5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76014, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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     "status": "ok",
     "timestamp": 1703772175927,
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    },
    "id": "VaruNWAquzuH",
    "outputId": "3386214d-78a0-474e-87b6-8c5f52d861cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mid                 int64\n",
       "date               object\n",
       "venue              object\n",
       "bat_team           object\n",
       "bowl_team          object\n",
       "batsman            object\n",
       "bowler             object\n",
       "runs                int64\n",
       "wickets             int64\n",
       "overs             float64\n",
       "runs_last_5         int64\n",
       "wickets_last_5      int64\n",
       "striker             int64\n",
       "non-striker         int64\n",
       "total               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "cn2fPCOqvDWS"
   },
   "outputs": [],
   "source": [
    "# remove unwanted columns\n",
    "# keeping only consistent teams\n",
    "# removing the first 5 overs data in every match\n",
    "# conve date col into datetime obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1703772520834,
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    },
    "id": "ZS3MvVmhwWhg",
    "outputId": "ab742a73-169f-4cb7-e7cc-6e8f57b30473"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mid', 'date', 'venue', 'bat_team', 'bowl_team', 'batsman', 'bowler',\n",
       "       'runs', 'wickets', 'overs', 'runs_last_5', 'wickets_last_5', 'striker',\n",
       "       'non-striker', 'total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 769,
     "status": "ok",
     "timestamp": 1703772751306,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
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    },
    "id": "EwHekua8wXrT"
   },
   "outputs": [],
   "source": [
    "# remove unwanted columns\n",
    "columns_to_remove = ['mid', 'venue', 'batsman', 'bowler', 'striker', 'non-striker']\n",
    "df.drop(labels=columns_to_remove,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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    "outputId": "fbc9841c-a35e-46fd-c228-5c1847d9690f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76014, 9)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1703772847920,
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    },
    "id": "FK7N2cihxXke",
    "outputId": "c8c8aef1-b0e5-445d-e766-86ed38cf8d28"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Kolkata Knight Riders', 'Chennai Super Kings', 'Rajasthan Royals',\n",
       "       'Mumbai Indians', 'Deccan Chargers', 'Kings XI Punjab',\n",
       "       'Royal Challengers Bangalore', 'Delhi Daredevils',\n",
       "       'Kochi Tuskers Kerala', 'Pune Warriors', 'Sunrisers Hyderabad',\n",
       "       'Rising Pune Supergiants', 'Gujarat Lions',\n",
       "       'Rising Pune Supergiant'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bat_team'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 824,
     "status": "ok",
     "timestamp": 1703773108737,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
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    },
    "id": "VcC_Crmhih1r"
   },
   "outputs": [],
   "source": [
    "# keeping only consistent teams\n",
    "consistent_teams = ['Kolkata Knight Riders', 'Chennai Super Kings', 'Rajasthan Royals',\n",
    "                    'Mumbai Indians', 'Kings XI Punjab', 'Royal Challengers Bangalore',\n",
    "                    'Delhi Daredevils', 'Sunrisers Hyderabad']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 551,
     "status": "ok",
     "timestamp": 1703773112074,
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      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "4F32tgnpyCIC"
   },
   "outputs": [],
   "source": [
    "df = df[(df['bat_team'].isin(consistent_teams)) & (df['bowl_team'].isin(consistent_teams))]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53811, 9)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1703773235235,
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    },
    "id": "rWGS9Vzpyw7u",
    "outputId": "6b4cd745-2ff2-4d1b-eb04-11f21add7fd2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Kolkata Knight Riders', 'Chennai Super Kings', 'Rajasthan Royals',\n",
       "       'Mumbai Indians', 'Kings XI Punjab', 'Royal Challengers Bangalore',\n",
       "       'Delhi Daredevils', 'Sunrisers Hyderabad'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"bat_team\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "executionInfo": {
     "elapsed": 498,
     "status": "ok",
     "timestamp": 1703773360468,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "kpxI7BokzGMY"
   },
   "outputs": [],
   "source": [
    "# removing the first 5 overs data in every match\n",
    "df=df[df['overs']>5.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "executionInfo": {
     "elapsed": 528,
     "status": "ok",
     "timestamp": 1703773374487,
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    "id": "WbYdBGoUznSR",
    "outputId": "5c4cf806-2b86-4b03-aa81-e6056d61e3a7"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40088, 9)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "executionInfo": {
     "elapsed": 774,
     "status": "ok",
     "timestamp": 1703773403087,
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     },
     "user_tz": -330
    },
    "id": "NITJPLYAzsZm"
   },
   "outputs": [],
   "source": [
    "# conve date col into datetime obj\n",
    "df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 40088 entries, 32 to 75888\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   date            40088 non-null  datetime64[ns]\n",
      " 1   bat_team        40088 non-null  object        \n",
      " 2   bowl_team       40088 non-null  object        \n",
      " 3   runs            40088 non-null  int64         \n",
      " 4   wickets         40088 non-null  int64         \n",
      " 5   overs           40088 non-null  float64       \n",
      " 6   runs_last_5     40088 non-null  int64         \n",
      " 7   wickets_last_5  40088 non-null  int64         \n",
      " 8   total           40088 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(5), object(2)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "executionInfo": {
     "elapsed": 812,
     "status": "ok",
     "timestamp": 1703773494863,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "7mUKZCFYzxIN"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 892
    },
    "executionInfo": {
     "elapsed": 1667,
     "status": "ok",
     "timestamp": 1703773520567,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "try0O4SSxjBE",
    "outputId": "7de5dbac-84a8-4fe6-f78d-64290c7b5189"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dataframe with only numeric columns\n",
    "df_numeric = df.select_dtypes(include=[np.number])\n",
    "\n",
    "# Get correlation of all the features of the dataset\n",
    "corr_matrix = df_numeric.corr()\n",
    "top_corr_features = corr_matrix.index\n",
    "\n",
    "# Plotting the heatmap\n",
    "plt.figure(figsize=(13,10))\n",
    "g = sns.heatmap(data=df_numeric[top_corr_features].corr(), annot=True, cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "1T3UrE170gbL"
   },
   "outputs": [],
   "source": [
    "#Data preprocessing\n",
    "# Handling categorical(object) features\n",
    "# Splitting dataset into train and test set on the basis of date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "TlRlViii0wyo"
   },
   "outputs": [],
   "source": [
    "# to convert caterigerical featuers into numerical featuers we use one hot encoding technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 580,
     "status": "ok",
     "timestamp": 1703773801684,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "PpEegAdv1Eb3",
    "outputId": "56d0ddb4-1896-461e-906e-0edc7902d3c8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'runs', 'wickets', 'overs', 'runs_last_5', 'wickets_last_5',\n",
       "       'total', 'bat_team_Chennai Super Kings', 'bat_team_Delhi Daredevils',\n",
       "       'bat_team_Kings XI Punjab', 'bat_team_Kolkata Knight Riders',\n",
       "       'bat_team_Mumbai Indians', 'bat_team_Rajasthan Royals',\n",
       "       'bat_team_Royal Challengers Bangalore', 'bat_team_Sunrisers Hyderabad',\n",
       "       'bowl_team_Chennai Super Kings', 'bowl_team_Delhi Daredevils',\n",
       "       'bowl_team_Kings XI Punjab', 'bowl_team_Kolkata Knight Riders',\n",
       "       'bowl_team_Mumbai Indians', 'bowl_team_Rajasthan Royals',\n",
       "       'bowl_team_Royal Challengers Bangalore',\n",
       "       'bowl_team_Sunrisers Hyderabad'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df = pd.get_dummies(data=df, columns=['bat_team', 'bowl_team'])\n",
    "encoded_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 377
    },
    "executionInfo": {
     "elapsed": 111,
     "status": "ok",
     "timestamp": 1703773885987,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "hq_L2DPx1jaX",
    "outputId": "f98e7e2d-fb62-4575-8fae-74189c4a1fbb"
   },
   "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>date</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>total</th>\n",
       "      <th>bat_team_Chennai Super Kings</th>\n",
       "      <th>bat_team_Delhi Daredevils</th>\n",
       "      <th>bat_team_Kings XI Punjab</th>\n",
       "      <th>...</th>\n",
       "      <th>bat_team_Royal Challengers Bangalore</th>\n",
       "      <th>bat_team_Sunrisers Hyderabad</th>\n",
       "      <th>bowl_team_Chennai Super Kings</th>\n",
       "      <th>bowl_team_Delhi Daredevils</th>\n",
       "      <th>bowl_team_Kings XI Punjab</th>\n",
       "      <th>bowl_team_Kolkata Knight Riders</th>\n",
       "      <th>bowl_team_Mumbai Indians</th>\n",
       "      <th>bowl_team_Rajasthan Royals</th>\n",
       "      <th>bowl_team_Royal Challengers Bangalore</th>\n",
       "      <th>bowl_team_Sunrisers Hyderabad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>5.2</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>222</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>222</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>222</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>222</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  runs  wickets  overs  runs_last_5  wickets_last_5  total  \\\n",
       "32 2008-04-18    61        0    5.1           59               0    222   \n",
       "33 2008-04-18    61        1    5.2           59               1    222   \n",
       "34 2008-04-18    61        1    5.3           59               1    222   \n",
       "35 2008-04-18    61        1    5.4           59               1    222   \n",
       "36 2008-04-18    61        1    5.5           58               1    222   \n",
       "\n",
       "    bat_team_Chennai Super Kings  bat_team_Delhi Daredevils  \\\n",
       "32                         False                      False   \n",
       "33                         False                      False   \n",
       "34                         False                      False   \n",
       "35                         False                      False   \n",
       "36                         False                      False   \n",
       "\n",
       "    bat_team_Kings XI Punjab  ...  bat_team_Royal Challengers Bangalore  \\\n",
       "32                     False  ...                                 False   \n",
       "33                     False  ...                                 False   \n",
       "34                     False  ...                                 False   \n",
       "35                     False  ...                                 False   \n",
       "36                     False  ...                                 False   \n",
       "\n",
       "    bat_team_Sunrisers Hyderabad  bowl_team_Chennai Super Kings  \\\n",
       "32                         False                          False   \n",
       "33                         False                          False   \n",
       "34                         False                          False   \n",
       "35                         False                          False   \n",
       "36                         False                          False   \n",
       "\n",
       "    bowl_team_Delhi Daredevils  bowl_team_Kings XI Punjab  \\\n",
       "32                       False                      False   \n",
       "33                       False                      False   \n",
       "34                       False                      False   \n",
       "35                       False                      False   \n",
       "36                       False                      False   \n",
       "\n",
       "    bowl_team_Kolkata Knight Riders  bowl_team_Mumbai Indians  \\\n",
       "32                            False                     False   \n",
       "33                            False                     False   \n",
       "34                            False                     False   \n",
       "35                            False                     False   \n",
       "36                            False                     False   \n",
       "\n",
       "    bowl_team_Rajasthan Royals  bowl_team_Royal Challengers Bangalore  \\\n",
       "32                       False                                   True   \n",
       "33                       False                                   True   \n",
       "34                       False                                   True   \n",
       "35                       False                                   True   \n",
       "36                       False                                   True   \n",
       "\n",
       "    bowl_team_Sunrisers Hyderabad  \n",
       "32                          False  \n",
       "33                          False  \n",
       "34                          False  \n",
       "35                          False  \n",
       "36                          False  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "executionInfo": {
     "elapsed": 536,
     "status": "ok",
     "timestamp": 1703774147911,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "dVj9eyGJj5-J"
   },
   "outputs": [],
   "source": [
    "# Rearranging the columns\n",
    "encoded_df = encoded_df[['date', 'bat_team_Chennai Super Kings', 'bat_team_Delhi Daredevils', 'bat_team_Kings XI Punjab',\n",
    "              'bat_team_Kolkata Knight Riders', 'bat_team_Mumbai Indians', 'bat_team_Rajasthan Royals',\n",
    "              'bat_team_Royal Challengers Bangalore', 'bat_team_Sunrisers Hyderabad',\n",
    "              'bowl_team_Chennai Super Kings', 'bowl_team_Delhi Daredevils', 'bowl_team_Kings XI Punjab',\n",
    "              'bowl_team_Kolkata Knight Riders', 'bowl_team_Mumbai Indians', 'bowl_team_Rajasthan Royals',\n",
    "              'bowl_team_Royal Challengers Bangalore', 'bowl_team_Sunrisers Hyderabad',\n",
    "              'overs', 'runs', 'wickets', 'runs_last_5', 'wickets_last_5', 'total']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "executionInfo": {
     "elapsed": 519,
     "status": "ok",
     "timestamp": 1703774730974,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "2W25Y0pf2mTS"
   },
   "outputs": [],
   "source": [
    "# spliting the data into train and test set\n",
    "X_train = encoded_df.drop(labels='total', axis=1)[encoded_df['date'].dt.year <= 2016]\n",
    "X_test = encoded_df.drop(labels='total', axis=1)[encoded_df['date'].dt.year >= 2017]\n",
    "\n",
    "y_train = encoded_df[encoded_df['date'].dt.year <= 2016]['total'].values\n",
    "y_test = encoded_df[encoded_df['date'].dt.year >= 2017]['total'].values\n",
    "\n",
    "# Removing the 'date' column\n",
    "X_train.drop(labels='date', axis=True, inplace=True)\n",
    "X_test.drop(labels='date', axis=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 29,
     "status": "ok",
     "timestamp": 1703774749013,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "g_qwaYI940nY",
    "outputId": "70da34b0-e2ed-46f6-8f63-536a99515ddf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37310, 21)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 564,
     "status": "ok",
     "timestamp": 1703774810341,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "ENzZEfTS43vt",
    "outputId": "50c35f16-a228-4b83-d808-937f28ae3fcf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2778, 21)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "wbpUnrmB5UvU"
   },
   "outputs": [],
   "source": [
    "#Modeling building\n",
    "\n",
    "#Applying These Machine learning (supervised learning) Algorithm:-\n",
    "#   linear regression\n",
    "#   Decision Tree Regression\n",
    "#   Random forest regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "executionInfo": {
     "elapsed": 539,
     "status": "ok",
     "timestamp": 1703775365786,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "EtBlFWpJ6J0R",
    "outputId": "f4b9ff40-3c71-4f23-9c5d-a8141710b7dd"
   },
   "outputs": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Appling linear regression algorithm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "linear_regressor= LinearRegression()\n",
    "linear_regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "executionInfo": {
     "elapsed": 549,
     "status": "ok",
     "timestamp": 1703775440341,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "SpAqA7GT7TAs"
   },
   "outputs": [],
   "source": [
    "y_pred_lr=linear_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "executionInfo": {
     "elapsed": 561,
     "status": "ok",
     "timestamp": 1703775575258,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "CaU4kxkN7wCa",
    "outputId": "868453b6-d982-4f78-a45e-726615164a83"
   },
   "outputs": [
    {
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       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree Regression Model\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "decision_regressor = DecisionTreeRegressor()\n",
    "decision_regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "executionInfo": {
     "elapsed": 558,
     "status": "ok",
     "timestamp": 1703775610654,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "4GL5bDOx8I3R"
   },
   "outputs": [],
   "source": [
    "y_pred_dt = decision_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
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     "status": "ok",
     "timestamp": 1703775715377,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "zAeeKPBu8L1N",
    "outputId": "6bd20263-2b06-4de6-c998-79188010d49a"
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Regression Model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "random_regressor = RandomForestRegressor()\n",
    "random_regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "executionInfo": {
     "elapsed": 522,
     "status": "ok",
     "timestamp": 1703775724817,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "Tkoj7Iwt8lXf"
   },
   "outputs": [],
   "source": [
    "y_pred_rf = random_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "executionInfo": {
     "elapsed": 509,
     "status": "ok",
     "timestamp": 1703775940555,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "-C4QKIa-yhYB"
   },
   "outputs": [],
   "source": [
    "def predict_score(batting_team='Chennai Super Kings', bowling_team='Mumbai Indians', overs=5.1, runs=50, wickets=0, runs_in_prev_5=50, wickets_in_prev_5=0):\n",
    "  temp_array = list()\n",
    "\n",
    "  # Batting Team\n",
    "  if batting_team == 'Chennai Super Kings':\n",
    "    temp_array = temp_array + [1,0,0,0,0,0,0,0]\n",
    "  elif batting_team == 'Delhi Daredevils':\n",
    "    temp_array = temp_array + [0,1,0,0,0,0,0,0]\n",
    "  elif batting_team == 'Kings XI Punjab':\n",
    "    temp_array = temp_array + [0,0,1,0,0,0,0,0]\n",
    "  elif batting_team == 'Kolkata Knight Riders':\n",
    "    temp_array = temp_array + [0,0,0,1,0,0,0,0]\n",
    "  elif batting_team == 'Mumbai Indians':\n",
    "    temp_array = temp_array + [0,0,0,0,1,0,0,0]\n",
    "  elif batting_team == 'Rajasthan Royals':\n",
    "    temp_array = temp_array + [0,0,0,0,0,1,0,0]\n",
    "  elif batting_team == 'Royal Challengers Bangalore':\n",
    "    temp_array = temp_array + [0,0,0,0,0,0,1,0]\n",
    "  elif batting_team == 'Sunrisers Hyderabad':\n",
    "    temp_array = temp_array + [0,0,0,0,0,0,0,1]\n",
    "\n",
    "  # Bowling Team\n",
    "  if bowling_team == 'Chennai Super Kings':\n",
    "    temp_array = temp_array + [1,0,0,0,0,0,0,0]\n",
    "  elif bowling_team == 'Delhi Daredevils':\n",
    "    temp_array = temp_array + [0,1,0,0,0,0,0,0]\n",
    "  elif bowling_team == 'Kings XI Punjab':\n",
    "    temp_array = temp_array + [0,0,1,0,0,0,0,0]\n",
    "  elif bowling_team == 'Kolkata Knight Riders':\n",
    "    temp_array = temp_array + [0,0,0,1,0,0,0,0]\n",
    "  elif bowling_team == 'Mumbai Indians':\n",
    "    temp_array = temp_array + [0,0,0,0,1,0,0,0]\n",
    "  elif bowling_team == 'Rajasthan Royals':\n",
    "    temp_array = temp_array + [0,0,0,0,0,1,0,0]\n",
    "  elif bowling_team == 'Royal Challengers Bangalore':\n",
    "    temp_array = temp_array + [0,0,0,0,0,0,1,0]\n",
    "  elif bowling_team == 'Sunrisers Hyderabad':\n",
    "    temp_array = temp_array + [0,0,0,0,0,0,0,1]\n",
    "\n",
    "  # Overs, Runs, Wickets, Runs_in_prev_5, Wickets_in_prev_5\n",
    "  temp_array = temp_array + [overs, runs, wickets, runs_in_prev_5, wickets_in_prev_5]\n",
    "\n",
    "  # Converting into numpy array\n",
    "  temp_array = np.array([temp_array])\n",
    "\n",
    "  # Prediction\n",
    "  return int(linear_regressor.predict(temp_array)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 522,
     "status": "ok",
     "timestamp": 1703776373951,
     "user": {
      "displayName": "aravind reddy",
      "userId": "05063739861446158750"
     },
     "user_tz": -330
    },
    "id": "fhgissiE10Bx",
    "outputId": "1b184f4f-4977-4c09-a48d-b0452ba9bb00"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The final predicted score (range): 182 to 197\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kamal\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "final_score = predict_score(batting_team='Chennai Super Kings', bowling_team='Royal Challengers Bangalore', overs=9.5, runs=100, wickets=2, runs_in_prev_5=60, wickets_in_prev_5=0)\n",
    "print(\"The final predicted score (range): {} to {}\".format(final_score-10, final_score+5))"
   ]
  }
 ],
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