stateWiseAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model for State wise  Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "def pre(total_pop):\n",
    "    temp=[]\n",
    "    for i in total_pop:\n",
    "        k=str(i).split(',')\n",
    "        x=\"\"\n",
    "        for j in k:\n",
    "            x+=j\n",
    "        temp.append(int(x))\n",
    "    return temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data uploading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"state_pop.csv\")\n",
    "df2=pd.read_csv(\"percapita.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>States</th>\n",
       "      <th>three_lpa_pop</th>\n",
       "      <th>ten_lpa_pop</th>\n",
       "      <th>tenPlus_pop</th>\n",
       "      <th>S. No.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh (AP)</td>\n",
       "      <td>18,959,462</td>\n",
       "      <td>885,993</td>\n",
       "      <td>195,535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>227,690</td>\n",
       "      <td>10,463</td>\n",
       "      <td>2,297</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Assam</td>\n",
       "      <td>5,447,201</td>\n",
       "      <td>226,697</td>\n",
       "      <td>30,217</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bihar (BI)</td>\n",
       "      <td>16,883,440</td>\n",
       "      <td>258,752</td>\n",
       "      <td>34,447</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chhattisgarh (CH)</td>\n",
       "      <td>4,267,675</td>\n",
       "      <td>186,996</td>\n",
       "      <td>36,386</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                States three_lpa_pop ten_lpa_pop tenPlus_pop  S. No.\n",
       "1  Andhra Pradesh (AP)    18,959,462     885,993     195,535       1\n",
       "2    Arunachal Pradesh       227,690      10,463       2,297       2\n",
       "3                Assam     5,447,201     226,697      30,217       3\n",
       "4           Bihar (BI)    16,883,440     258,752      34,447       4\n",
       "6    Chhattisgarh (CH)     4,267,675     186,996      36,386       5"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(\"S. No.\",inplace=True,ascending=True,kind='quicksort')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S. No.</th>\n",
       "      <th>State/UT</th>\n",
       "      <th>Per Capita Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>93699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>103633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Assam</td>\n",
       "      <td>54618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>31380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>78001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   S. No.           State/UT  Per Capita Income\n",
       "0       1     Andhra Pradesh              93699\n",
       "1       2  Arunachal Pradesh             103633\n",
       "2       3              Assam              54618\n",
       "3       4              Bihar              31380\n",
       "4       5       Chhattisgarh              78001"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 32033]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "middle_pop=pre(df[\"ten_lpa_pop\"].values)\n",
    "upper_pop=pre(df[\"tenPlus_pop\"].values)\n",
    "total=[]\n",
    "for i in range(len(middle_pop)):\n",
    "    total.append(int(middle_pop[i])+int(upper_pop[i]))\n",
    "total[:32]     #total population that can bear byuing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "per_capita=df2[\"Per Capita Income\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Commodity Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"flipkart_com-ecommerce_sample.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "uid=df[\"uniq_id\"].values\n",
    "p_name=df[\"product_name\"].values\n",
    "price=df[\"retail_price\"].values\n",
    "d_price=df[\"discounted_price\"].values\n",
    "brand=df[\"brand\"].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "brand1=[]\n",
    "for i in range(len(brand)):\n",
    "    k=p_name[i].split()\n",
    "    try: \n",
    "        if(math.isnan(brand[i])):\n",
    "            brand1.append(k[0])\n",
    "    except:\n",
    "        brand1.append(brand[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "csv=pd.DataFrame({\"uniq_id\":uid,\"product_name\":p_name,\"retail_price\":price,\"discounted_price\":d_price,\"brand\":brand1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "csv.to_csv(\"dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "csv[\"retail_price\"]=csv[\"retail_price\"].fillna(csv[\"retail_price\"].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "csv[\"discounted_price\"]=csv[\"discounted_price\"].fillna(csv[\"discounted_price\"].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "csv.to_csv(\"dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "discounted_price=csv[\"discounted_price\"].values\n",
    "retail_price=csv[\"retail_price\"].values\n",
    "uid=csv[\"uniq_id\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"input_vector=[]\n",
    "for i in range(0,int(len(uid)//2)):\n",
    "    l=[]\n",
    "    for j in range(len(total)):\n",
    "        l.append([[retail_price[i]],[total[j]],[per_capita[j]]])\n",
    "    input_vector.append(l)\n",
    "test_input_vector=[]\n",
    "for i in range(len(uid)//2,len(uid)):\n",
    "    l=[]\n",
    "    for j in range(len(total)):\n",
    "        l.append([[retail_price[i]],[total[j]],[per_capita[j]]])\n",
    "    test_input_vector.append(l)\n",
    "\"\"\"\n",
    "input_vector=[]\n",
    "for i in range(0,len(uid)):\n",
    "    for j in range(len(total)):\n",
    "        input_vector.append([retail_price[i],total[j],per_capita[j]])\n",
    "len(input_vector[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       " 2200.0,\n",
       " 4375.0,\n",
       " ...]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"label=[]\n",
    "for i in range(len(discounted_price)):\n",
    "    l=[]\n",
    "    for j in range(len(total)):\n",
    "        l.append([discounted_price[i] *100 /per_capita[j]])\n",
    "    label.append(l)\n",
    "len(label[0])\n",
    "\"\"\"\n",
    "label=[]\n",
    "for i in range(len(discounted_price)):\n",
    "    label.append(discounted_price[i])\n",
    "label\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Multi-layer Perceptron regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = MLPRegressor(solver='adam', alpha=0.0001,hidden_layer_sizes=(5, 3), random_state=0, learning_rate=\"constant\", learning_rate_init=0.06,activation=\"relu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(5, 3), learning_rate='constant',\n",
       "       learning_rate_init=0.06, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=0, shuffle=True,\n",
       "       solver='adam', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(input_vector[0:len(label)//2],label[0:len(label)//2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "k=clf.predict(input_vector[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e/askY8nAw9k6T3XyhSe+wI+P/ZjaeC2/ftWv8+vbfp2mdNOMoysO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\nEtj/D3mQHBWskRe7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x71a578a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(k,label[:1000], c=\"green\",s=30, alpha=0.1,label=(\"predicted\",\"actual\"))\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
}