DATA602 Removing Missing Data.ipynb

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   "outputs": [],
   "source": [
    "# REMOVING MISSING DATA\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# Load the file\n",
    "df = pd.read_csv(\"d:/machine learning/Banking_Marketing.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               float64\n",
       "job                object\n",
       "marital            object\n",
       "education          object\n",
       "default            object\n",
       "housing            object\n",
       "loan               object\n",
       "contact            object\n",
       "month              object\n",
       "day_of_week        object\n",
       "duration          float64\n",
       "campaign            int64\n",
       "pdays               int64\n",
       "previous            int64\n",
       "poutcome           object\n",
       "emp_var_rate      float64\n",
       "cons_price_idx    float64\n",
       "cons_conf_idx     float64\n",
       "euribor3m         float64\n",
       "nr_employed       float64\n",
       "y                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the datatype of each column\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               2\n",
       "job               0\n",
       "marital           0\n",
       "education         0\n",
       "default           0\n",
       "housing           0\n",
       "loan              0\n",
       "contact           6\n",
       "month             0\n",
       "day_of_week       0\n",
       "duration          7\n",
       "campaign          0\n",
       "pdays             0\n",
       "previous          0\n",
       "poutcome          0\n",
       "emp_var_rate      0\n",
       "cons_price_idx    0\n",
       "cons_conf_idx     0\n",
       "euribor3m         0\n",
       "nr_employed       0\n",
       "y                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To find the missing values for each column, we use the isna() function provided by pandas\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Once you have figured out all the missing details, we remove all the missing rows from the DataFrame. \n",
    "# To do so, we use the dropna() function\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean for age is:  40.02537208342438\n"
     ]
    }
   ],
   "source": [
    "# Compute the mean age\n",
    "mean_age = df.age.mean()\n",
    "print(\"The mean for age is: \", mean_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute the missing data with its mean using the fillna() function.\n",
    "df.age.fillna(mean_age, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print median duration:  180.0\n"
     ]
    }
   ],
   "source": [
    "# Median duration\n",
    "median_duration = df.duration.median()\n",
    "print(\"Print median duration: \", median_duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute the missing data of the duration with its median using the fillna() function\n",
    "df. duration.fillna(median_duration,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode of Contact is: cellular\n"
     ]
    }
   ],
   "source": [
    "# Impute the categorical data of the contact column with its mode. To do so, first, find the mode of the contact \n",
    "# column using the mode() function of pandas\n",
    "mode_contact = df.contact.mode()[0]\n",
    "print(\"Mode of Contact is:\", mode_contact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute the missing data of the contact column with its mode using the fillna() function.\n",
    "df.contact.fillna(mode_contact,inplace=True)"
   ]
  }
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