HW1-solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CS381 Data Analytics: Homework 1 Answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandasql as ps\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading MPG dataset ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400, 9)\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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0  18.0          8         307.0       130.0    3504          12.0   \n",
       "1  15.0          8         350.0       165.0    3693          11.5   \n",
       "2  18.0          8         318.0       150.0    3436          11.0   \n",
       "3  16.0          8         304.0       150.0    3433          12.0   \n",
       "4  17.0          8         302.0       140.0    3449          10.5   \n",
       "\n",
       "   model_year origin                       name  \n",
       "0          70    usa  chevrolet chevelle malibu  \n",
       "1          70    usa          buick skylark 320  \n",
       "2          70    usa         plymouth satellite  \n",
       "3          70    usa              amc rebel sst  \n",
       "4          70    usa                ford torino  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg = pd.read_csv(\"mpg2.csv\")\n",
    "print(mpg.shape)\n",
    "mpg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1. Check if there are any null or NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg              True\n",
       "cylinders       False\n",
       "displacement    False\n",
       "horsepower       True\n",
       "weight          False\n",
       "acceleration    False\n",
       "model_year      False\n",
       "origin          False\n",
       "name            False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2. Remove any rows that has null or NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(393, 9)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg.dropna(inplace=True)\n",
    "mpg.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3. Check if there is any outliers in the mpg field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    393.000000\n",
      "mean      23.615267\n",
      "std        8.487261\n",
      "min        9.000000\n",
      "25%       17.000000\n",
      "50%       23.000000\n",
      "75%       29.000000\n",
      "max       90.000000\n",
      "Name: mpg, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1adab4a8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "print(mpg.mpg.describe())\n",
    "# use boxplot from dataframe\n",
    "mpg.boxplot(column=['mpg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ae8f908>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# or use seaborn\n",
    "sns.boxplot(x=mpg['mpg'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4. Display and then Remove the one outlier row. You should now have 392 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>90.0</td>\n",
       "      <td>8</td>\n",
       "      <td>451.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>2555</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>usa</td>\n",
       "      <td>tesla model Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "9  90.0          8         451.0       450.0    2555           8.0   \n",
       "\n",
       "   model_year origin           name  \n",
       "9        2020    usa  tesla model Y  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg[mpg.mpg > 60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(392, 9)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg = mpg[ mpg.mpg < 60]\n",
    "mpg.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 5. Calculate the average acceleration cars made in usa using SQL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Type in your code here**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 1 Answer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "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>Count</th>\n",
       "      <th>avg(acceleration)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>245</td>\n",
       "      <td>14.990204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Count  avg(acceleration)\n",
       "0    245          14.990204"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = \"select count(*) as Count, avg(acceleration) from mpg where origin = 'usa'\"\n",
    "df = ps.sqldf(sql)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 6. Calculate the average horsepower for each country using SQL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Type in your code here**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 6 Answer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   origin  avg(horsepower)\n",
      "0  europe        80.558824\n",
      "1   japan        79.835443\n",
      "2     usa       119.048980\n"
     ]
    }
   ],
   "source": [
    "print(ps.sqldf(\"select origin, avg(horsepower) from mpg group by origin\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 7. Calculate the number of occurrences, mean, min, max of the weight for each country using SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   origin  count(*)  avg(weight)  min(weight)  max(weight)\n",
      "0  europe        68  2433.470588         1825         3820\n",
      "1   japan        79  2221.227848         1613         2930\n",
      "2     usa       245  3372.489796         1800         5140\n"
     ]
    }
   ],
   "source": [
    "sql = \"select origin, count(*), avg(weight), min(weight), max(weight) from mpg group by origin\"\n",
    "print(ps.sqldf(sql))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 8. Answer Question 7 but use Pandas functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Type in your code here**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count\n",
      "origin\n",
      "europe     68\n",
      "japan      79\n",
      "usa       245\n",
      "Name: weight, dtype: int64\n",
      "Mean weight by origin\n",
      "origin\n",
      "europe    2433.470588\n",
      "japan     2221.227848\n",
      "usa       3372.489796\n",
      "Name: weight, dtype: float64\n",
      "Min weight by origin\n",
      "origin\n",
      "europe    1825\n",
      "japan     1613\n",
      "usa       1800\n",
      "Name: weight, dtype: int64\n",
      "Max weight by origin\n",
      "origin\n",
      "europe    3820\n",
      "japan     2930\n",
      "usa       5140\n",
      "Name: weight, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "gpby = mpg.groupby(['origin'])['weight']\n",
    "print(\"Count\")\n",
    "print(gpby.count())\n",
    "print(\"Mean weight by origin\")\n",
    "print(gpby.mean())\n",
    "print(\"Min weight by origin\")\n",
    "print(gpby.min())\n",
    "print(\"Max weight by origin\")\n",
    "print(gpby.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 9. Calculate the Standard deviation, Skew and Kurtosis for the horsepower, weight and accleartion for each country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STD\n",
      "        horsepower      weight  acceleration\n",
      "origin                                      \n",
      "europe   20.157871  491.813867      3.087813\n",
      "japan    17.819199  320.497248      1.954937\n",
      "usa      39.897790  795.346690      2.736020\n",
      "\n",
      "\n",
      "Skew\n",
      "        horsepower    weight  acceleration\n",
      "origin                                    \n",
      "europe    0.472850  0.812081      0.945656\n",
      "japan     0.660375  0.498497      0.042795\n",
      "usa       0.763248  0.067009      0.098737\n"
     ]
    }
   ],
   "source": [
    "gpby = mpg.groupby(['origin'])['horsepower','weight','acceleration']\n",
    "print(\"STD\")\n",
    "print(gpby.std())\n",
    "print(\"\\n\")\n",
    "print(\"Skew\")\n",
    "print(gpby.skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USA Kurtosis\n",
      "horsepower     -0.189521\n",
      "weight         -0.965494\n",
      "acceleration   -0.142774\n",
      "dtype: float64\n",
      "\n",
      "Europe Kurtosis\n",
      "horsepower     -0.059514\n",
      "weight         -0.249813\n",
      "acceleration    0.036290\n",
      "dtype: float64\n",
      "\n",
      "Japan Kurtosis\n",
      "horsepower     -0.131696\n",
      "weight         -0.380625\n",
      "acceleration   -0.528450\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"USA Kurtosis\")\n",
    "print(mpg[mpg.origin == 'usa'][['horsepower','weight','acceleration']].kurtosis())\n",
    "print(\"\\nEurope Kurtosis\")\n",
    "print(mpg[mpg.origin == 'europe'][['horsepower','weight','acceleration']].kurtosis())\n",
    "print(\"\\nJapan Kurtosis\")\n",
    "print(mpg[mpg.origin == 'japan'][['horsepower','weight','acceleration']].kurtosis())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 10. How would you describe the acceleration distribution for different country based on the Skew?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All has positive skew with Europe has highest skew, while Japan and USA has close to zero skew. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 11. How would you describe the acceleration distribution for different country based on Kurotsis?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "USA and Japan has negative kurtosis with Japan being more negative while Europe has positive kurtosis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 12. Plot the histogram of acceleration distribution for different country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a1b016a20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1b043d30>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a1b048198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1a7d65c0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpg['acceleration'].hist(by=mpg['origin'], bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 13. Calculate correlation of all the factors among themselves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.777618</td>\n",
       "      <td>-0.805127</td>\n",
       "      <td>-0.778427</td>\n",
       "      <td>-0.832244</td>\n",
       "      <td>0.423329</td>\n",
       "      <td>0.580541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>-0.777618</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.950823</td>\n",
       "      <td>0.842983</td>\n",
       "      <td>0.897527</td>\n",
       "      <td>-0.504683</td>\n",
       "      <td>-0.345647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>displacement</th>\n",
       "      <td>-0.805127</td>\n",
       "      <td>0.950823</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.897257</td>\n",
       "      <td>0.932994</td>\n",
       "      <td>-0.543800</td>\n",
       "      <td>-0.369855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>-0.778427</td>\n",
       "      <td>0.842983</td>\n",
       "      <td>0.897257</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.864538</td>\n",
       "      <td>-0.689196</td>\n",
       "      <td>-0.416361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>-0.832244</td>\n",
       "      <td>0.897527</td>\n",
       "      <td>0.932994</td>\n",
       "      <td>0.864538</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.416839</td>\n",
       "      <td>-0.309120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acceleration</th>\n",
       "      <td>0.423329</td>\n",
       "      <td>-0.504683</td>\n",
       "      <td>-0.543800</td>\n",
       "      <td>-0.689196</td>\n",
       "      <td>-0.416839</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.290316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year</th>\n",
       "      <td>0.580541</td>\n",
       "      <td>-0.345647</td>\n",
       "      <td>-0.369855</td>\n",
       "      <td>-0.416361</td>\n",
       "      <td>-0.309120</td>\n",
       "      <td>0.290316</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mpg  cylinders  displacement  horsepower    weight  \\\n",
       "mpg           1.000000  -0.777618     -0.805127   -0.778427 -0.832244   \n",
       "cylinders    -0.777618   1.000000      0.950823    0.842983  0.897527   \n",
       "displacement -0.805127   0.950823      1.000000    0.897257  0.932994   \n",
       "horsepower   -0.778427   0.842983      0.897257    1.000000  0.864538   \n",
       "weight       -0.832244   0.897527      0.932994    0.864538  1.000000   \n",
       "acceleration  0.423329  -0.504683     -0.543800   -0.689196 -0.416839   \n",
       "model_year    0.580541  -0.345647     -0.369855   -0.416361 -0.309120   \n",
       "\n",
       "              acceleration  model_year  \n",
       "mpg               0.423329    0.580541  \n",
       "cylinders        -0.504683   -0.345647  \n",
       "displacement     -0.543800   -0.369855  \n",
       "horsepower       -0.689196   -0.416361  \n",
       "weight           -0.416839   -0.309120  \n",
       "acceleration      1.000000    0.290316  \n",
       "model_year        0.290316    1.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 14. Which factors (cylinders, horsepower or weight) will affect your mileage the most based on correlation?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weight affect mpg the most"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 15. Does the result depend on the country? If yes, how?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "USA\n",
      "                   mpg  cylinders  displacement  horsepower    weight  \\\n",
      "mpg           1.000000  -0.824524     -0.834628   -0.751570 -0.846424   \n",
      "cylinders    -0.824524   1.000000      0.933885    0.827646  0.881609   \n",
      "displacement -0.834628   0.933885      1.000000    0.902744  0.917588   \n",
      "horsepower   -0.751570   0.827646      0.902744    1.000000  0.838450   \n",
      "weight       -0.846424   0.881609      0.917588    0.838450  1.000000   \n",
      "acceleration  0.377239  -0.563293     -0.619890   -0.719191 -0.440230   \n",
      "model_year    0.648641  -0.463986     -0.497591   -0.495009 -0.406388   \n",
      "\n",
      "              acceleration  model_year  \n",
      "mpg               0.377239    0.648641  \n",
      "cylinders        -0.563293   -0.463986  \n",
      "displacement     -0.619890   -0.497591  \n",
      "horsepower       -0.719191   -0.495009  \n",
      "weight           -0.440230   -0.406388  \n",
      "acceleration      1.000000    0.380878  \n",
      "model_year        0.380878    1.000000  \n",
      "\n",
      "Japan\n",
      "                   mpg  cylinders  displacement  horsepower    weight  \\\n",
      "mpg           1.000000  -0.139788     -0.366020   -0.673095 -0.564106   \n",
      "cylinders    -0.139788   1.000000      0.720992    0.431770  0.489184   \n",
      "displacement -0.366020   0.720992      1.000000    0.730176  0.841430   \n",
      "horsepower   -0.673095   0.431770      0.730176    1.000000  0.867589   \n",
      "weight       -0.564106   0.489184      0.841430    0.867589  1.000000   \n",
      "acceleration  0.401114  -0.219675     -0.535590   -0.720149 -0.567682   \n",
      "model_year    0.568662   0.085985      0.120835   -0.215264  0.045365   \n",
      "\n",
      "              acceleration  model_year  \n",
      "mpg               0.401114    0.568662  \n",
      "cylinders        -0.219675    0.085985  \n",
      "displacement     -0.535590    0.120835  \n",
      "horsepower       -0.720149   -0.215264  \n",
      "weight           -0.567682    0.045365  \n",
      "acceleration      1.000000   -0.000944  \n",
      "model_year       -0.000944    1.000000  \n",
      "\n",
      "Europe\n",
      "                   mpg  cylinders  displacement  horsepower    weight  \\\n",
      "mpg           1.000000  -0.271719     -0.495594   -0.679575 -0.512011   \n",
      "cylinders    -0.271719   1.000000      0.657791    0.392653  0.574767   \n",
      "displacement -0.495594   0.657791      1.000000    0.622043  0.891560   \n",
      "horsepower   -0.679575   0.392653      0.622043    1.000000  0.611619   \n",
      "weight       -0.512011   0.574767      0.891560    0.611619  1.000000   \n",
      "acceleration  0.298047   0.024453      0.038032   -0.544388  0.166325   \n",
      "model_year    0.504231   0.262851      0.206940   -0.132330  0.178178   \n",
      "\n",
      "              acceleration  model_year  \n",
      "mpg               0.298047    0.504231  \n",
      "cylinders         0.024453    0.262851  \n",
      "displacement      0.038032    0.206940  \n",
      "horsepower       -0.544388   -0.132330  \n",
      "weight            0.166325    0.178178  \n",
      "acceleration      1.000000    0.175077  \n",
      "model_year        0.175077    1.000000  \n"
     ]
    }
   ],
   "source": [
    "print(\"\\nUSA\")\n",
    "print(mpg[mpg['origin'] == 'usa'].corr())\n",
    "print(\"\\nJapan\")\n",
    "print(mpg[mpg['origin'] == 'japan'].corr())\n",
    "print(\"\\nEurope\")\n",
    "print(mpg[mpg['origin'] == 'europe'].corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, results depend on country. For car made in Japan and Europe, horsepower is a stronger factor while weight is the strongest factor for cars made in USA"
   ]
  }
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