diff --git a/TechChallange.ipynb b/TechChallange.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "9712600f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "94aefe46",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = ('data/measurements.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "e4ddf56b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "69a6679e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " distance | \n",
+ " consume | \n",
+ " speed | \n",
+ " temp_inside | \n",
+ " temp_outside | \n",
+ " specials | \n",
+ " gas_type | \n",
+ " AC | \n",
+ " rain | \n",
+ " sun | \n",
+ " refill liters | \n",
+ " refill gas | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 28 | \n",
+ " 5 | \n",
+ " 26 | \n",
+ " 21,5 | \n",
+ " 12 | \n",
+ " NaN | \n",
+ " E10 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 45 | \n",
+ " E10 | \n",
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+ " \n",
+ " 1 | \n",
+ " 12 | \n",
+ " 4,2 | \n",
+ " 30 | \n",
+ " 21,5 | \n",
+ " 13 | \n",
+ " NaN | \n",
+ " E10 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
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+ " \n",
+ " 2 | \n",
+ " 11,2 | \n",
+ " 5,5 | \n",
+ " 38 | \n",
+ " 21,5 | \n",
+ " 15 | \n",
+ " NaN | \n",
+ " E10 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
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+ " \n",
+ " 3 | \n",
+ " 12,9 | \n",
+ " 3,9 | \n",
+ " 36 | \n",
+ " 21,5 | \n",
+ " 14 | \n",
+ " NaN | \n",
+ " E10 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 18,5 | \n",
+ " 4,5 | \n",
+ " 46 | \n",
+ " 21,5 | \n",
+ " 15 | \n",
+ " NaN | \n",
+ " E10 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " distance consume speed temp_inside temp_outside specials gas_type AC \\\n",
+ "0 28 5 26 21,5 12 NaN E10 0 \n",
+ "1 12 4,2 30 21,5 13 NaN E10 0 \n",
+ "2 11,2 5,5 38 21,5 15 NaN E10 0 \n",
+ "3 12,9 3,9 36 21,5 14 NaN E10 0 \n",
+ "4 18,5 4,5 46 21,5 15 NaN E10 0 \n",
+ "\n",
+ " rain sun refill liters refill gas \n",
+ "0 0 0 45 E10 \n",
+ "1 0 0 NaN NaN \n",
+ "2 0 0 NaN NaN \n",
+ "3 0 0 NaN NaN \n",
+ "4 0 0 NaN NaN "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "548f1460",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " speed | \n",
+ " temp_outside | \n",
+ " AC | \n",
+ " rain | \n",
+ " sun | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " count | \n",
+ " 388.000000 | \n",
+ " 388.000000 | \n",
+ " 388.000000 | \n",
+ " 388.000000 | \n",
+ " 388.000000 | \n",
+ "
\n",
+ " \n",
+ " mean | \n",
+ " 41.927835 | \n",
+ " 11.358247 | \n",
+ " 0.077320 | \n",
+ " 0.123711 | \n",
+ " 0.082474 | \n",
+ "
\n",
+ " \n",
+ " std | \n",
+ " 13.598524 | \n",
+ " 6.991542 | \n",
+ " 0.267443 | \n",
+ " 0.329677 | \n",
+ " 0.275441 | \n",
+ "
\n",
+ " \n",
+ " min | \n",
+ " 14.000000 | \n",
+ " -5.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " 25% | \n",
+ " 32.750000 | \n",
+ " 7.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " 50% | \n",
+ " 40.500000 | \n",
+ " 10.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " 75% | \n",
+ " 50.000000 | \n",
+ " 16.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " max | \n",
+ " 90.000000 | \n",
+ " 31.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " speed temp_outside AC rain sun\n",
+ "count 388.000000 388.000000 388.000000 388.000000 388.000000\n",
+ "mean 41.927835 11.358247 0.077320 0.123711 0.082474\n",
+ "std 13.598524 6.991542 0.267443 0.329677 0.275441\n",
+ "min 14.000000 -5.000000 0.000000 0.000000 0.000000\n",
+ "25% 32.750000 7.000000 0.000000 0.000000 0.000000\n",
+ "50% 40.500000 10.000000 0.000000 0.000000 0.000000\n",
+ "75% 50.000000 16.000000 0.000000 0.000000 0.000000\n",
+ "max 90.000000 31.000000 1.000000 1.000000 1.000000"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "1b6d4f28",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(388, 12)"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "ef497cc5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['speed', 'temp_outside', 'AC', 'rain', 'sun']"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "numeric_cols = df.select_dtypes(include=\"number\").columns.tolist()\n",
+ "numeric_cols"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "35ce9772",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['distance',\n",
+ " 'consume',\n",
+ " 'temp_inside',\n",
+ " 'specials',\n",
+ " 'gas_type',\n",
+ " 'refill liters',\n",
+ " 'refill gas']"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "categorical_cols = df.select_dtypes(exclude=\"number\").columns.tolist()\n",
+ "categorical_cols"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "dfdef80a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "distance 0\n",
+ "consume 0\n",
+ "speed 0\n",
+ "temp_inside 12\n",
+ "temp_outside 0\n",
+ "specials 295\n",
+ "gas_type 0\n",
+ "AC 0\n",
+ "rain 0\n",
+ "sun 0\n",
+ "refill liters 375\n",
+ "refill gas 375\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "dacd29e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# drop specials column\n",
+ "df.drop(\"specials\", axis=1, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "1c139961",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 21\n",
+ "1 21\n",
+ "2 21\n",
+ "3 21\n",
+ "4 21\n",
+ " ..\n",
+ "383 24\n",
+ "384 25\n",
+ "385 25\n",
+ "386 25\n",
+ "387 25\n",
+ "Name: temp_inside, Length: 388, dtype: object"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# format Total_amount, column to be two decimal places\n",
+ "df[\"temp_inside\"] = df[\"temp_inside\"].map('{:.2}'.format)\n",
+ "df[\"temp_inside\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "60058be5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# fill the na values in the temp_insode column with the mean\n",
+ "df[\"temp_inside\"] = pd.to_numeric(df[\"temp_inside\"],errors=\"coerce\").fillna(df[\"temp_inside\"].astype(float).mean()).astype(int)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "54db1037",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 21\n",
+ "1 21\n",
+ "2 21\n",
+ "3 21\n",
+ "4 21\n",
+ " ..\n",
+ "383 24\n",
+ "384 25\n",
+ "385 25\n",
+ "386 25\n",
+ "387 25\n",
+ "Name: temp_inside, Length: 388, dtype: int32"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[\"temp_inside\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "0a0b36fe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# replace the \",\" with \".\" in distance and consume\n",
+ "df[\"distance\"]=df[\"distance\"].str.replace(',', '.')\n",
+ "df[\"consume\"]=df[\"consume\"].str.replace(',', '.')\n",
+ "df[\"refill liters\"]=df[\"refill liters\"].str.replace(',', '.')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "610110c7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# convert to float\n",
+ "df[\"distance\"]=df[\"distance\"].astype(float)\n",
+ "df[\"consume\"]=df[\"consume\"].astype(float)\n",
+ "df[\"refill liters\"]=df[\"refill liters\"].astype(float)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "35429a16",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
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+ "