stage w - Regression of 50_startups_csv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set(rc={'figure.figsize': [10, 10]}, font_scale=1.3, palette=\"husl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0  165349.20       136897.80        471784.10    New York  192261.83\n",
       "1  162597.70       151377.59        443898.53  California  191792.06\n",
       "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3  144372.41       118671.85        383199.62    New York  182901.99\n",
       "4  142107.34        91391.77        366168.42     Florida  166187.94"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Regression/50_Startups.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   R&D Spend        50 non-null     float64\n",
      " 1   Administration   50 non-null     float64\n",
      " 2   Marketing Spend  50 non-null     float64\n",
      " 3   State            50 non-null     object \n",
      " 4   Profit           50 non-null     float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 2.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>73721.615600</td>\n",
       "      <td>121344.639600</td>\n",
       "      <td>211025.097800</td>\n",
       "      <td>112012.639200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45902.256482</td>\n",
       "      <td>28017.802755</td>\n",
       "      <td>122290.310726</td>\n",
       "      <td>40306.180338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>51283.140000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14681.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39936.370000</td>\n",
       "      <td>103730.875000</td>\n",
       "      <td>129300.132500</td>\n",
       "      <td>90138.902500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>73051.080000</td>\n",
       "      <td>122699.795000</td>\n",
       "      <td>212716.240000</td>\n",
       "      <td>107978.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>101602.800000</td>\n",
       "      <td>144842.180000</td>\n",
       "      <td>299469.085000</td>\n",
       "      <td>139765.977500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>165349.200000</td>\n",
       "      <td>182645.560000</td>\n",
       "      <td>471784.100000</td>\n",
       "      <td>192261.830000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           R&D Spend  Administration  Marketing Spend         Profit\n",
       "count      50.000000       50.000000        50.000000      50.000000\n",
       "mean    73721.615600   121344.639600    211025.097800  112012.639200\n",
       "std     45902.256482    28017.802755    122290.310726   40306.180338\n",
       "min         0.000000    51283.140000         0.000000   14681.400000\n",
       "25%     39936.370000   103730.875000    129300.132500   90138.902500\n",
       "50%     73051.080000   122699.795000    212716.240000  107978.190000\n",
       "75%    101602.800000   144842.180000    299469.085000  139765.977500\n",
       "max    165349.200000   182645.560000    471784.100000  192261.830000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    17\n",
       "New York      17\n",
       "Florida       16\n",
       "Name: State, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['State'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe448759848>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['State'], y=df['Profit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.histplot(data=df, x=\"Profit\", hue=\"State\", multiple=\"dodge\", binwidth=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}