Rating Products and Customers(2nd).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X=np.array([[np.random.randint(200),np.random.randint(15,100)] for x in range(300)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BlWE5Lg+ojmmvFXtMfe3rG+lDpFl07ZjNl4/tzZeP7c3mnXt5bd46Js9aw6Rp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23cc86e0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range(1, 5):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1, 5), wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AE1XjhFo36vg0m3Hlo1c5b3quKh2zc6c5l66sLnRlHLLuF07RTdhW/KZRnYKYeKOZjF/v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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23ccb14efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters =4, init = 'k-means++', random_state =3)\n",
    "y_kmeans = kmeans.fit_predict(X)\n",
    "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Best')\n",
    "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Nice')\n",
    "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Good')\n",
    "plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Low')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Status=[]\n",
    "for i in y_kmeans:\n",
    "    if(i==0):\n",
    "        Status.append('Best')\n",
    "    elif(i==1):\n",
    "        Status.append('Nice')\n",
    "    elif(i==2):\n",
    "        Status.append('Good')\n",
    "    elif(i==3):\n",
    "        Status.append('Low')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('Mall_Customers.csv','w') as f:\n",
    "    writer=csv.writer(f)\n",
    "    writer.writerow(['user_id','Salary','Spending','Type'])\n",
    "    for i in range(206210):    \n",
    "        j=np.random.randint(300)\n",
    "        Tuple=[i]\n",
    "        Tuple.append(X[j][0])\n",
    "        Tuple.append(X[j][1])\n",
    "        Tuple.append(j)\n",
    "        writer.writerow(Tuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "users=pd.read_csv('Mall_Customers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "users=users.dropna(axis=0, how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "users.to_csv('Mall_Customers.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Userproducts=pd.read_csv('UserProducts.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Score=pd.merge(Userproducts,users,on=['user_id','user_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ProductRating=Score.groupby(['product_id','Type']).size().reset_index(name='counts').sort_values('counts',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(\"ProductRating.csv\", \"w\") as file:\n",
    "    writer = csv.writer(file)\n",
    "    writer.writerow(['product_id','type'])\n",
    "    for i in range(1,49689):\n",
    "        ProductDetails=ProductRating[(ProductRating['product_id']==i)]\n",
    "        Total=sum(ProductDetails['counts'])\n",
    "        List=ProductDetails['Type'].values\n",
    "        Type=['Low','Good','Nice','Best']\n",
    "        Hash=dict()\n",
    "        for id in range(len(List)):\n",
    "            for j in Type:\n",
    "                if List[id]==j:\n",
    "                    Hash[j]=ProductDetails['counts'].values[id]\n",
    "        for Priority in Type:\n",
    "            try:\n",
    "                if Hash[Priority]>=0.2*Total:\n",
    "                    writer.writerow([i,Priority])\n",
    "                    break\n",
    "            except:\n",
    "                pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ProductScore=pd.read_csv('ProductRating.csv')\n",
    "ProductScore=ProductScore.dropna(axis=0, how='all')\n",
    "ProductScore.to_csv('ProductRating.csv',index=False)"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}