Assignment1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('data_missingValues.csv')#reading the input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zero_data = dataset.iloc[:,6:7].values#selecting only the required row\n",
    "mean_data = dataset.iloc[:,6:7].values\n",
    "median_data = dataset.iloc[:,6:7].values\n",
    "most_frequent_data = dataset.iloc[:,6:7].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zero_data = np.delete(zero_data,0)#deletinig the headiing row and only numbers/floats are left\n",
    "mean_data= np.delete(mean_data,0)\n",
    "median_data = np.delete(median_data,0)\n",
    "most_frequent_data = np.delete(most_frequent_data,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zero_data[pd.isnull(zero_data)]  = 0 #replaceing nans with zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mean_data = mean_data.reshape(-1,1)\n",
    "median_data = median_data.reshape(-1,1)\n",
    "most_frequent_data = most_frequent_data.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer #replaceing nans with mean values\n",
    "imputer = Imputer(missing_values = 'NaN',strategy = 'mean', axis = 0)\n",
    "imputer.fit(mean_data[:])\n",
    "mean_data[:] = imputer.transform(mean_data[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer #replaceing nans with median\n",
    "imputer = Imputer(missing_values = 'NaN',strategy = 'median', axis = 0)\n",
    "imputer.fit(median_data[:])\n",
    "median_data[:] = imputer.transform(median_data[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer #replaceing nans with mode\n",
    "imputer = Imputer(missing_values = 'NaN',strategy = 'most_frequent', axis = 0)\n",
    "imputer.fit(most_frequent_data[:])\n",
    "most_frequent_data[:] = imputer.transform(most_frequent_data[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def guassianCurve(mean,std): \n",
    "    return (1/(std * np.sqrt(2 * np.pi)) * np.exp( - (x - mean)**2 / (2 * std**2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mean_zero_data = np.mean(zero_data, axis=0)\n",
    "std_zero_data = np.std(zero_data, axis=0)\n",
    "mean_mean_data = np.mean(mean_data,axis=0)\n",
    "std_mean_data = np.std(mean_data, axis=0)\n",
    "mean_median_data = np.mean(median_data,axis=0)\n",
    "std_median_data = np.std(median_data,axis=0)\n",
    "mean_most_frequent_data = np.mean(most_frequent_data,axis = 0)\n",
    "std_most_frequent_data = np.std(most_frequent_data,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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iUJL+dCC4JGCX/7GjuQJ4POh+lojUishLInLhcC8QkSv9y9S2tLSEEJIxiWXbNvAMpjKL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\nzTiBss7TgHUi8jruBO9Vqjr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff957661438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x = np.arange(0.0,5.0,0.1)\n",
    "# plt.figure(figsize=(10,10))\n",
    "x=np.linspace(-5,25,100)\n",
    "plt.title(\"Guassian distribution curve\")\n",
    "plt.plot(x,guassianCurve(mean_zero_data,std_zero_data),'r')\n",
    "plt.plot(x,guassianCurve(mean_mean_data,std_mean_data),'g')\n",
    "plt.plot(x,guassianCurve(mean_median_data,std_median_data),'m')\n",
    "plt.plot(x,guassianCurve(mean_most_frequent_data,std_most_frequent_data),'b')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}