Stat-exercise-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b6ad3019",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "url = 'https://raw.githubusercontent.com/codebasics/math-for-machine-learning/main/3_normal_distribution/Exercise/bhp.csv'\n",
    "res = requests.get(url, allow_redirects = True)\n",
    "with open(\"banglore_price.csv\", 'wb') as file:\n",
    "    file.write(res.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "36e3b830",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size  total_sqft  bath   price  bhk  \\\n",
       "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2   \n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4   \n",
       "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3   \n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3   \n",
       "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2   \n",
       "\n",
       "   price_per_sqft  \n",
       "0            3699  \n",
       "1            4615  \n",
       "2            4305  \n",
       "3            6245  \n",
       "4            4250  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"banglore_price.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "04173e38",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.320000e+04\n",
       "mean     7.920337e+03\n",
       "std      1.067272e+05\n",
       "min      2.670000e+02\n",
       "25%      4.267000e+03\n",
       "50%      5.438000e+03\n",
       "75%      7.317000e+03\n",
       "max      1.200000e+07\n",
       "Name: price_per_sqft, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.price_per_sqft.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f2a136e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist(df.price_per_sqft, bins = 20, rwidth=0.8)\n",
    "plt.xlabel(\"Price per sq feet\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "047d15e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1366.184, 50959.36200000098)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll, ul = df.price_per_sqft.quantile([0.001, 0.999])\n",
    "ll, ul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c7ff33a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 7)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers = df[(df.price_per_sqft < ll) | (df.price_per_sqft > ul)]\n",
    "outliers.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9c3ba32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers.max().price_per_sqft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99fb77c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13172, 7)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_by_percentile = df[(df.price_per_sqft > ll) & (df.price_per_sqft < ul)]\n",
    "df_by_percentile.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e5a42e28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23227.73653589429, -9900.429065502549)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mal = df_by_percentile.price_per_sqft.mean() + 4*df_by_percentile.price_per_sqft.std()\n",
    "mil = df_by_percentile.price_per_sqft.mean() - 4*df_by_percentile.price_per_sqft.std()\n",
    "mal, mil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c011bf35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13047, 7)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_by_std = df_by_percentile[(df_by_percentile.price_per_sqft > mil) & \n",
    "                             (df_by_percentile.price_per_sqft < mal)]\n",
    "df_by_std.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4ed07fd1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_by_std.price_per_sqft, bins = 20, rwidth=0.8)\n",
    "plt.xlabel('Price per square ft')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6e2d5fbb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Price'), Text(0, 0.5, 'Count')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style = 'whitegrid')\n",
    "g = sns.histplot(x=df_by_std.price_per_sqft, data = df_by_std, kde=True)\n",
    "g.set(xlabel = \"Price\", ylabel = \"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abee4126",
   "metadata": {},
   "source": [
    "# Z-Score : (x - mean)/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9763a017",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-13-c2f73ee6b762>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_by_percentile['z-score'] = (df_by_percentile.price_per_sqft - df_by_percentile.price_per_sqft.mean()) / df_by_percentile.price_per_sqft.std()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "      <th>z-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9677</th>\n",
       "      <td>Marathahalli</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1270.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>2</td>\n",
       "      <td>5748</td>\n",
       "      <td>-0.221118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10178</th>\n",
       "      <td>Electronic City</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1065.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.76</td>\n",
       "      <td>2</td>\n",
       "      <td>2888</td>\n",
       "      <td>-0.911769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7222</th>\n",
       "      <td>Hebbal</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1355.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>83.87</td>\n",
       "      <td>3</td>\n",
       "      <td>6189</td>\n",
       "      <td>-0.114622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2261</th>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1255.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>65.26</td>\n",
       "      <td>2</td>\n",
       "      <td>5200</td>\n",
       "      <td>-0.353452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3074</th>\n",
       "      <td>Hosa Road</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1541.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>69.84</td>\n",
       "      <td>3</td>\n",
       "      <td>4532</td>\n",
       "      <td>-0.514765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5101</th>\n",
       "      <td>Seegehalli</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1096.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>2</td>\n",
       "      <td>3740</td>\n",
       "      <td>-0.706022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6786</th>\n",
       "      <td>Ardendale</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1777.26</td>\n",
       "      <td>3.0</td>\n",
       "      <td>105.00</td>\n",
       "      <td>3</td>\n",
       "      <td>5907</td>\n",
       "      <td>-0.182722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8186</th>\n",
       "      <td>Electronic City</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1470.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44.10</td>\n",
       "      <td>3</td>\n",
       "      <td>3000</td>\n",
       "      <td>-0.884722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Yelahanka</td>\n",
       "      <td>5 BHK</td>\n",
       "      <td>3050.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>213.00</td>\n",
       "      <td>5</td>\n",
       "      <td>6983</td>\n",
       "      <td>0.077118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11473</th>\n",
       "      <td>other</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4000</td>\n",
       "      <td>-0.643236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4969</th>\n",
       "      <td>Electronic City</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1090.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35.03</td>\n",
       "      <td>2</td>\n",
       "      <td>3213</td>\n",
       "      <td>-0.833286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9174</th>\n",
       "      <td>Kanakpura Road</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>700.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>2</td>\n",
       "      <td>5857</td>\n",
       "      <td>-0.194796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6087</th>\n",
       "      <td>other</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2100.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>90.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4285</td>\n",
       "      <td>-0.574412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4897</th>\n",
       "      <td>Bannerghatta</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>2370.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>195.00</td>\n",
       "      <td>3</td>\n",
       "      <td>8227</td>\n",
       "      <td>0.377527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11521</th>\n",
       "      <td>Horamavu Agara</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1220.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>2</td>\n",
       "      <td>3196</td>\n",
       "      <td>-0.837391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                location       size  total_sqft  bath   price  bhk  \\\n",
       "9677        Marathahalli      2 BHK     1270.00   2.0   73.00    2   \n",
       "10178    Electronic City      2 BHK     1065.00   2.0   30.76    2   \n",
       "7222              Hebbal      3 BHK     1355.00   3.0   83.87    3   \n",
       "2261   Bannerghatta Road      2 BHK     1255.00   2.0   65.26    2   \n",
       "3074           Hosa Road      3 BHK     1541.00   3.0   69.84    3   \n",
       "5101          Seegehalli      2 BHK     1096.00   2.0   41.00    2   \n",
       "6786           Ardendale      3 BHK     1777.26   3.0  105.00    3   \n",
       "8186     Electronic City      3 BHK     1470.00   2.0   44.10    3   \n",
       "139            Yelahanka      5 BHK     3050.00   5.0  213.00    5   \n",
       "11473              other      2 BHK     1500.00   2.0   60.00    2   \n",
       "4969     Electronic City      2 BHK     1090.00   2.0   35.03    2   \n",
       "9174      Kanakpura Road      2 BHK      700.00   1.0   41.00    2   \n",
       "6087               other  4 Bedroom     2100.00   4.0   90.00    4   \n",
       "4897        Bannerghatta      3 BHK     2370.00   4.0  195.00    3   \n",
       "11521     Horamavu Agara      2 BHK     1220.00   2.0   39.00    2   \n",
       "\n",
       "       price_per_sqft   z-score  \n",
       "9677             5748 -0.221118  \n",
       "10178            2888 -0.911769  \n",
       "7222             6189 -0.114622  \n",
       "2261             5200 -0.353452  \n",
       "3074             4532 -0.514765  \n",
       "5101             3740 -0.706022  \n",
       "6786             5907 -0.182722  \n",
       "8186             3000 -0.884722  \n",
       "139              6983  0.077118  \n",
       "11473            4000 -0.643236  \n",
       "4969             3213 -0.833286  \n",
       "9174             5857 -0.194796  \n",
       "6087             4285 -0.574412  \n",
       "4897             8227  0.377527  \n",
       "11521            3196 -0.837391  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_by_percentile['z-score'] = (df_by_percentile.price_per_sqft - df_by_percentile.price_per_sqft.mean()) / df_by_percentile.price_per_sqft.std()\n",
    "df_by_percentile.sample(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2a965cd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13047, 8)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_by_z_score = df_by_percentile[(df_by_percentile['z-score'] > -4) & (df_by_percentile['z-score'] < 4)]\n",
    "df_by_z_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "299e57be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_by_std.shape[0] - df_by_z_score.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4667a9a5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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