From 8cf3fc821e9d2c506e2f03e1b09442254de5cce0 Mon Sep 17 00:00:00 2001 From: Sampath N Date: Mon, 13 Dec 2021 21:12:02 +0530 Subject: [PATCH 1/3] First write --- ml_task.ipynb | 665 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 665 insertions(+) create mode 100644 ml_task.ipynb diff --git a/ml_task.ipynb b/ml_task.ipynb new file mode 100644 index 0000000..6216c13 --- /dev/null +++ b/ml_task.ipynb @@ -0,0 +1,665 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "DATA PREPROCESSING " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.read_csv('dataset_housingprices.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -122.23 37.88 41 880 129.0 \n", + "1 -122.22 37.86 21 7099 1106.0 \n", + "2 -122.24 37.85 52 1467 190.0 \n", + "3 -122.25 37.85 52 1274 235.0 \n", + "4 -122.25 37.85 52 1627 280.0 \n", + "\n", + " population households median_income median_house_value \n", + "0 322 126 8.3252 452600 \n", + "1 2401 1138 8.3014 358500 \n", + "2 496 177 7.2574 352100 \n", + "3 558 219 5.6431 341300 \n", + "4 565 259 3.8462 342200 \n" + ] + } + ], + "source": [ + "print(df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(20640, 9)\n" + ] + } + ], + "source": [ + "print(df.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.drop_duplicates(inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
count20640.00000020640.00000020640.00000020640.00000020433.00000020640.00000020640.00000020640.00000020640.000000
mean-119.56970435.63186128.6394862635.763081537.8705531425.476744499.5396803.870671206855.816909
std2.0035322.13595212.5855582181.615252421.3850701132.462122382.3297531.899822115395.615874
min-124.35000032.5400001.0000002.0000001.0000003.0000001.0000000.49990014999.000000
25%-121.80000033.93000018.0000001447.750000296.000000787.000000280.0000002.563400119600.000000
50%-118.49000034.26000029.0000002127.000000435.0000001166.000000409.0000003.534800179700.000000
75%-118.01000037.71000037.0000003148.000000647.0000001725.000000605.0000004.743250264725.000000
max-114.31000041.95000052.00000039320.0000006445.00000035682.0000006082.00000015.000100500001.000000
\n", + "
" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms \\\n", + "count 20640.000000 20640.000000 20640.000000 20640.000000 \n", + "mean -119.569704 35.631861 28.639486 2635.763081 \n", + "std 2.003532 2.135952 12.585558 2181.615252 \n", + "min -124.350000 32.540000 1.000000 2.000000 \n", + "25% -121.800000 33.930000 18.000000 1447.750000 \n", + "50% -118.490000 34.260000 29.000000 2127.000000 \n", + "75% -118.010000 37.710000 37.000000 3148.000000 \n", + "max -114.310000 41.950000 52.000000 39320.000000 \n", + "\n", + " total_bedrooms population households median_income \\\n", + "count 20433.000000 20640.000000 20640.000000 20640.000000 \n", + "mean 537.870553 1425.476744 499.539680 3.870671 \n", + "std 421.385070 1132.462122 382.329753 1.899822 \n", + "min 1.000000 3.000000 1.000000 0.499900 \n", + "25% 296.000000 787.000000 280.000000 2.563400 \n", + "50% 435.000000 1166.000000 409.000000 3.534800 \n", + "75% 647.000000 1725.000000 605.000000 4.743250 \n", + "max 6445.000000 35682.000000 6082.000000 15.000100 \n", + "\n", + " median_house_value \n", + "count 20640.000000 \n", + "mean 206855.816909 \n", + "std 115395.615874 \n", + "min 14999.000000 \n", + "25% 119600.000000 \n", + "50% 179700.000000 \n", + "75% 264725.000000 \n", + "max 500001.000000 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n", + " 'total_bedrooms', 'population', 'households', 'median_income',\n", + " 'median_house_value'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "columns=df.columns\n", + "print(columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "check the datatype of the columns and the number of entries in the each" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 20640 entries, 0 to 20639\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 longitude 20640 non-null float64\n", + " 1 latitude 20640 non-null float64\n", + " 2 housing_median_age 20640 non-null int64 \n", + " 3 total_rooms 20640 non-null int64 \n", + " 4 total_bedrooms 20433 non-null float64\n", + " 5 population 20640 non-null int64 \n", + " 6 households 20640 non-null int64 \n", + " 7 median_income 20640 non-null float64\n", + " 8 median_house_value 20640 non-null int64 \n", + "dtypes: float64(4), int64(5)\n", + "memory usage: 1.4 MB\n", + "None\n" + ] + } + ], + "source": [ + "info=df.info()\n", + "print(info)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, in several instances columns containing numeric data can have “object” data type. Some special characters like ? or space can be present amidst continuous variables because of lack of data points. We will check the unique entries in such cases to remove them and change the data types:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The unique values in longitude are [-122.23 -122.22 -122.24 -122.25 -122.26 -122.27 -122.28 -122.29 -122.3\n", + " -122.21 -122.2 -122.19 -122.18 -122.13 -122.16 -122.17 -122.15 -122.14\n", + " -122.12 -122.33 -122.34 -122.06 -122.07 -122.08 -122.09 -122.1 -122.11\n", + " -122.03 -121.97 -122.02 -122.04 -122.05 -121.99 -122.01 -121.96 -121.98\n", + " -122. -121.93 -121.94 -121.95 -121.92 -121.89 -121.91 -121.9 -121.88\n", + " -121.87 -121.85 -121.86 -121.84 -121.82 -121.77 -121.62 -121.61 -121.72\n", + " -121.73 -121.75 -121.8 -121.76 -121.78 -121.79 -119.78 -119.93 -120.\n", + " -120.56 -120.59 -120.55 -120.25 -120.79 -120.8 -120.65 -120.76 -120.88\n", + " -120.69 -120.93 -120.97 -120.87 -120.98 -120.72 -120.77 -120.66 -120.62\n", + " -120.71 -121.83 -121.81 -121.74 -121.68 -121.54 -121.51 -121.59 -121.58\n", + " -121.6 -121.63 -121.57 -121.65 -121.64 -121.71 -121.66 -121.56 -121.5\n", + " -121.41 -121.39 -121.24 -121.19 -121.36 -121.46 -121.49 -121.44 -121.47\n", + " -121.53 -121.52 -121.55 -121.67 -121.69 -121.7 -120.46 -120.54 -120.67\n", + " -120.9 -120.91 -120.57 -120.43 -120.42 -120.41 -120.36 -120.34 -120.33\n", + " -120.37 -120.27 -120.19 -122.51 -122.32 -122.36 -122.31 -122.39 -122.37\n", + " -122.41 -122.35 -122.38 -122.42 -124.17 -124.3 -124.23 -124.21 -124.19\n", + " -124.22 -124.16 -124.14 -124.15 -123.91 -123.83 -123.92 -119.94 -119.95\n", + " -119.97 -119.98 -119.96 -119.99 -120.01 -120.02 -119.92 -120.04 -120.03\n", + " -120.13 -120.16 -120.06 -120.1 -121.04 -120.92 -120.84 -120.81 -120.5\n", + " -120.3 -121.09 -121.08 -121.07 -121.06 -121. -121.01 -120.99 -121.02\n", + " -120.95 -120.96 -120.86 -120.83 -120.78 -120.7 -120.58 -120.6 -120.63\n", + " -120.44 -120.32 -120.08 -120.85 -119.81 -119.79 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+ " -118.27 -118.34 -117.65 -118.15 -118.17 -118.19 -118.16 -118.51 -118.66\n", + " -118.46 -118.83 -118.85 -118.82 -119.66 -120.14 -120.12 -122.89 -122.9\n", + " -122.91 -122.88 -123.07 -122.95 -122.92 -122.94 -122.99 -122.7 -122.87\n", + " -122.86 -122.83 -122.79 -122.8 -122.78 -122.69 -122.73 -122.66 -122.65\n", + " -122.52 -122.68 -122.63 -122.62 -122.61 -122.6 -122.64 -122.75 -122.71\n", + " -122.85 -122.84 -122.77 -122.72 -122.48 -122.59 -122.5 -122.55 -121.11\n", + " -121.03 -120.64 -120.49 -120.2 -118.28 -118.29 -118.35 -118.32 -118.36\n", + " -118.38 -118.49 -118.52 -118.54 -118.55 -118.57 -118.53 -118.63 -118.62\n", + " -118.64 -118.56 -118.58 -118.37 -118.65 -118.22 -118.2 -118.21 -118.24\n", + " -118.25 -118.26 -117.71 -117.78 -117.8 -117.83 -117.93 -117.91 -117.89\n", + " -117.88 -117.85 -117.86 -117.77 -117.75 -117.72 -117.92 -117.94 -117.97\n", + " -117.96 -118.02 -118.03 -118.04 -118.07 -118.08 -118.09 -118.1 -118.11\n", + " -118.12 -118.13 -118.14 -118.69 -118.67 -118.68 -118.76 -118.75 -118.72\n", + " -118.78 -118.8 -118.84 -118.79 -118.74 -118.86 -118.7 -119.51 -120.26\n", + " -120.29 -120.11 -122.49 -122.54 -122.58 -122.57 -122.56 -122.44 -122.45\n", + " -122.47 -122.46 -122.93 -122.96 -122.81 -120.15 -123.15 -123.24 -123.23\n", + " -123.47 -123.71 -123.58 -123.5 -123.64 -123.79 -123.8 -123.34 -123.4\n", + " -123.32 -123.38 -123.35 -123.37 -123.36 -123.1 -123.11 -123.18 -123.22\n", + " -123.21 -123.19 -123.2 -123.81 -123.7 -123.53 -123.69 -123.59 -123.54\n", + " -123.39 -123.17 -123.16 -120.68 -120.75 -120.74 -120.73 -120.94 -120.89\n", + " -120.61 -120.48 -120.47 -120.4 -120.24 -120.82 -121.16 -121.18 -119.3\n", + " -121.43 -121.45 -121.42 -121.48 -121.31 -121.32 -121.33 -121.4 -121.23\n", + " -121.25 -121.26 -121.12 -121.13 -121.2 -122.4 -121.1 -121.05 -121.22\n", + " -121.14 -121.21 -121.15 -120.17 -120.23 -117.62 -117.6 -117.63 -117.55\n", + " -117.59 -117.58 -117.49 -117.53 -117.61 -121.17 -121.27 -121.28 -121.3\n", + " -121.29 -120.53 -117.35 -117.36 -117.37 -117.38 -117.39 -117.41 -117.4\n", + " -117.44 -117.43 -117.42 -117.45 -117.5 -117.48 -117.47 -117.51 -117.52\n", + " -117.56 -117.57 -117.46 -117.54 -117.33 -117.34 -117.26 -117.3 -117.28\n", + " -117.32 -117.31 -117.29 -117.24 -117.23 -117.25 -117.21 -117.14 -117.27\n", + " -117.22 -117.16 -117.13 -117.19 -117.17 -117.2 -117.07 -117.11 -117.08\n", + " -117.18 -117.06 -117.09 -117.15 -117.12 -117.05 -116.96 -117.1 -116.99\n", + " -116.91 -116.89 -116.95 -116.9 -116.92 -116.93 -116.94 -116.97 -116.98\n", + " -117.01 -117. -116.87 -116.88 -116.86 -117.04 -117.03 -116.79 -116.77\n", + " -116.81 -116.75 -116.8 -116.71 -116.68 -116.74 -116.72 -116.48 -116.57\n", + " -116.76 -116.42 -116.6 -116.69 -116.39 -116.51 -116.61 -116.44 -116.36\n", + " -116.52 -116.53 -116.5 -116.47 -116.63 -116.54 -116.55 -116.49 -116.56\n", + " -116.46 -116.43 -116.45 -116.4 -116.38 -116.33 -116.31 -116.37 -116.41\n", + " -116.29 -116.3 -116.26 -116.25 -116.24 -116.21 -116.23 -116.2 -116.15\n", + " -116.11 -116.17 -116.12 -115.84 -116.16 -116.19 -116.18 -116.08 -115.22\n", + " -114.67 -114.49 -114.68 -114.56 -114.57 -114.59 -114.61 -114.6 -114.58\n", + " -114.62 -121.38 -121.37 -121.35 -121.34 -115.93 -115.75 -116.14 -116.32\n", + " -116.27 -116.35 -116.62 -116.73 -116.06 -116.09 -116.02 -115.85 -114.94\n", + " -114.47 -114.31 -114.64 -116.85 -116.83 -116.82 -116.84 -116.78 -116.58\n", + " -116.66 -116.67 -116.34 -116.28 -122.43 -120.52 -122.76 -123.26 -123.41\n", + " -123.08 -122.67 -122.82 -123.04 -123.02 -122.98 -123.01 -123. -122.97\n", + " -123.03 -123.49 -123.25 -123.48 -123.28 -123.13 -123.12 -123.43 -119.37\n", + " -118.73 -120.28 -119.17 -118.89 -118.81 -118.77 -118.71]\n", + "The unique values in latitude are [37.88 37.86 37.85 37.84 37.83 37.82 37.81 37.8 37.79 37.77 37.78 37.76\n", + " 37.75 37.74 37.73 37.9 37.89 37.87 37.72 37.71 37.7 37.69 37.68 37.64\n", + " 37.63 37.66 37.65 37.67 37.61 37.62 37.6 37.59 37.58 37.57 37.49 37.52\n", + " 37.56 37.55 37.54 37.53 37.51 37.48 37.47 37.5 38.69 38.72 38.52 38.48\n", + " 38.45 38.46 38.43 38.55 38.54 38.51 38.5 38.47 38.44 38.42 38.37 38.34\n", + " 38.32 38.26 38.38 38.4 38.39 38.36 38.31 39.76 39.78 39.77 39.74 39.75\n", + " 39.73 39.71 39.72 39.7 39.82 39.79 39.68 39.64 39.66 39.59 39.88 40.06\n", + " 39.97 39.86 39.83 39.8 39.69 39.61 39.65 39.55 39.52 39.53 39.6 39.54\n", + " 39.5 39.49 39.51 39.48 39.47 39.45 39.44 39.43 39.4 39.39 39.33 39.37\n", + " 39.35 39.34 39.32 39.36 39.38 39.42 39.41 38.15 38.12 38.09 38.07 37.97\n", + " 38.24 38.2 38.16 38.11 38.28 38.19 38.25 38.41 38.33 38.35 38.21 38.23\n", + " 38.29 39.03 38.99 39. 39.15 39.22 39.25 39.1 39.13 39.31 39.3 39.21\n", + " 38.03 38.04 38. 37.98 37.99 37.93 37.94 37.95 37.96 37.91 38.01 38.02\n", + " 38.05 37.92 38.06 41.8 41.75 41.77 41.78 41.73 41.76 41.74 41.95 41.92\n", + " 41.84 41.81 41.68 41.88 41.54 38.96 38.95 38.94 38.93 38.92 38.91 38.89\n", + " 38.9 38.88 38.87 38.84 38.76 38.86 39.06 39.04 39.01 38.81 38.83 38.85\n", + " 38.68 38.67 38.66 38.7 38.58 38.73 38.65 38.62 38.79 38.77 38.71 38.75\n", + " 38.74 38.6 38.61 38.57 38.53 38.8 38.63 36.73 36.74 36.72 36.75 36.71\n", + " 36.7 36.68 36.69 36.65 36.64 36.62 36.63 36.59 36.77 36.76 36.8 36.79\n", + " 36.81 36.78 36.66 36.83 36.85 36.82 36.84 36.86 36.91 36.87 36.94 36.88\n", + " 36.89 37.1 37.02 37. 37.25 37.13 37.09 37.12 37.11 37.06 36.61 36.6\n", + " 36.58 36.57 36.56 36.55 36.52 36.51 36.53 36.44 36.46 36.43 36.54 36.45\n", + " 36.34 36.16 36.21 36.19 36.29 36.18 36.14 36.15 36.13 36.49 36.97 40.8\n", + " 40.79 40.78 40.77 40.75 40.76 40.81 40.86 40.85 40.87 40.88 40.9 40.66\n", + " 40.91 41.03 41.32 41.09 41.11 41.3 41.01 41.36 41.13 41.06 41.04 40.97\n", + " 40.92 40.89 40.93 41.02 40.99 40.95 40.94 40.72 40.73 40.74 40.67 40.69\n", + " 40.62 40.6 40.59 40.55 40.57 40.48 40.58 40.5 40.44 40.47 40.45 40.54\n", + " 40.28 40.22 40.24 40.16 40.12 40.11 40.05 33.12 33.13 33.19 33.24 33.2\n", + " 33.09 33.04 32.99 32.96 32.98 32.97 32.82 32.76 32.86 32.81 32.85 32.84\n", + " 32.83 32.87 32.8 32.79 32.75 32.77 32.73 32.78 32.69 32.7 32.67 32.68\n", + " 33.33 32.93 32.74 33.41 33.4 33.38 33.32 33.36 33.34 33.3 33.28 33.26\n", + " 33.43 33.07 33.35 37.35 37.4 37.39 37.37 37.36 37.17 36.95 36.4 36.\n", + " 35.42 35.45 35.44 35.43 35.41 35.4 35.39 35.38 35.37 35.34 35.36 35.35\n", + " 35.32 35.33 35.3 35.31 35.27 35.22 35.24 35.28 34.82 34.83 34.81 34.95\n", + " 35.06 35.07 35.19 35.17 35.16 35.14 35.15 35.13 35.5 35.52 35.49 35.51\n", + " 35.6 35.55 35.58 35.59 35.62 35.65 35.76 35.64 35.68 35.67 35.78 35.77\n", + " 35.75 35.79 35.74 35.47 35.72 35.7 35.48 35.63 35.61 35.73 35.54 35.05\n", + " 35.03 35.21 35.12 35.08 35.1 34.92 34.86 34.99 35. 34.87 35.04 35.2\n", + " 35.26 35.23 36.41 36.37 36.38 36.32 36.31 36.3 36.33 36.27 36.28 36.35\n", + " 36.36 36.25 36.11 36.09 36.1 36.08 36.2 35.99 36.02 36.04 36.01 35.87\n", + " 35.91 39.23 39.18 39.17 39.14 39.12 39.11 39.09 39.08 39.02 39.07 39.05\n", + " 38.98 38.97 38.82 38.78 41.07 41.12 40.98 40.63 40.65 40.51 40.35 40.43\n", + " 40.42 40.41 40.36 40.37 40.38 40.29 40.31 40.17 40.26 39.92 34.27 34.26\n", + " 34.25 34.24 34.22 34.21 34.28 34.29 34.32 34.33 34.31 34.3 34.23 34.2\n", + " 34.19 34.18 34.17 34.16 34.15 34.14 34.13 34.12 34.11 34.1 34.09 34.08\n", + " 34.07 34.06 34.05 34.04 34.03 34.02 34.01 34. 33.99 33.98 33.97 33.94\n", + " 33.95 33.96 33.93 33.92 33.91 33.9 33.88 33.89 33.87 33.86 33.85 33.84\n", + " 33.83 33.82 33.81 33.8 33.79 33.78 33.77 33.76 33.75 33.74 33.73 33.72\n", + " 33.69 33.71 33.67 33.68 34.71 34.63 34.58 34.74 34.68 34.69 34.65 34.7\n", + " 34.67 34.72 34.76 34.77 34.66 34.62 34.73 34.59 34.57 34.53 34.61 34.6\n", + " 34.56 34.64 34.55 34.51 34.43 34.41 34.52 34.5 34.46 34.44 34.49 34.48\n", + " 34.45 34.4 34.47 34.42 34.39 34.38 34.37 34.36 37.34 37.32 37.29 37.21\n", + " 37.38 37.31 37.33 37.23 37.19 37.27 37.04 36.99 36.93 36.98 36.96 36.9\n", + " 36.92 38.1 38.14 38.13 38.08 38.18 37.43 37.46 39.81 39.67 39.46 39.26\n", + " 39.28 39.2 39.16 37.44 37.42 37.41 37.2 37.26 37.16 37.3 37.28 37.18\n", + " 37.01 37.08 37.07 37.05 37.03 41.31 41.35 41.82 41.79 41.4 41.61 36.67\n", + " 36.5 36.47 36.48 36.42 36.24 35.95 35.94 36.06 38.3 38.27 38.22 38.17\n", + " 38.49 38.56 38.59 39.24 39.19 39.27 39.29 33.61 33.62 33.6 33.63 33.59\n", + " 33.58 33.57 33.65 33.64 33.54 33.55 33.56 33.51 33.52 33.66 33.53 33.42\n", + " 33.47 33.45 33.46 33.44 33.48 33.49 33.5 33.7 39.93 39.9 39.95 39.96\n", + " 40.02 40.08 40.14 40.01 40.13 40.27 40.23 40.19 40.3 40.25 38.64 34.54\n", + " 34.34 34.91 34.9 34.89 34.35 34.85 34.84 34.98 34.75 34.88 34.94 34.96\n", + " 32.72 32.71 32.66 32.88 32.92 32.89 32.91 32.9 32.94 32.95 32.59 32.58\n", + " 32.57 32.56 32.55 32.54 32.63 32.65 32.64 32.62 32.61 32.6 33.01 33.\n", + " 33.02 33.03 33.05 33.06 33.08 33.17 33.16 33.15 33.1 33.11 33.18 33.14\n", + " 33.21 33.23 33.22 33.25 33.31 33.29 33.39 33.37 33.27 35.69 35.56 35.29\n", + " 35.25 35.11 35.18 35.02 35.46 37.45 37.24 34.93 34.97 37.22 37.15 37.14\n", + " 40.56 40.61 40.64 40.52 40.53 40.49 40.71 40.68 40.46 40.4 40.39 40.82\n", + " 39.56 39.63 41.86 41.66 41.6 41.7 41.69 41.72 41.63 41.53 41.48 41.46\n", + " 41.26 41.43 41.41 41.38 41.28 41.21 41.23 41.2 41.5 40.09 40.2 40.34\n", + " 40.32 40.18 40.15 40.07 40.03 39.94 39.91 41.15 36.39 36.22 36.23 36.12\n", + " 35.82 35.9 36.05 36.07 35.97 35.96 35.86 35.85 35.89 35.88]\n", + "The unique values in housing_median_age are [41 21 52 42 50 40 49 48 51 43 2 46 26 20 17 36 19 23 38 35 10 16 27 39\n", + " 31 29 22 37 28 34 32 47 44 30 18 45 33 24 15 14 13 25 5 12 6 8 9 7\n", + " 3 4 11 1]\n", + "The unique values in total_rooms are [ 880 7099 1467 ... 4598 272 10035]\n", + "The unique values in total_bedrooms are [ 129. 1106. 190. ... 3008. 1857. 1052.]\n", + "The unique values in population are [ 322 2401 496 ... 3060 2707 6912]\n", + "The unique values in households are [ 126 1138 177 ... 1767 1832 1818]\n", + "The unique values in median_income are [8.3252 8.3014 7.2574 ... 2.3598 2.3661 2.0943]\n", + "The unique values in median_house_value are [452600 358500 352100 ... 425800 200700 47000]\n" + ] + } + ], + "source": [ + "\n", + "for column in columns :\n", + " uni_val_col = df[column].unique()\n", + " print ('The unique values in ' , column , 'are ', uni_val_col)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creating separate continuous and categorical dataframes" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "## Create a dataframe with continuous columns \n", + "df_int = df.select_dtypes(include = ['int64']) \n", + "df_float = df.select_dtypes(include = ['float64'])\n", + "## Create a dataframe with categorical columns \n", + "df_cat = df.select_dtypes(include =['object'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " housing_median_age total_rooms population households median_house_value\n", + "0 41 880 322 126 452600\n", + "1 21 7099 2401 1138 358500\n", + "2 52 1467 496 177 352100\n", + "3 52 1274 558 219 341300\n", + "4 52 1627 565 259 342200\n", + " longitude latitude total_bedrooms median_income\n", + "0 -122.23 37.88 129.0 8.3252\n", + "1 -122.22 37.86 1106.0 8.3014\n", + "2 -122.24 37.85 190.0 7.2574\n", + "3 -122.25 37.85 235.0 5.6431\n", + "4 -122.25 37.85 280.0 3.8462\n", + "categorical dataframe\n", + "Empty DataFrame\n", + "Columns: []\n", + "Index: [0, 1, 2, 3, 4]\n" + ] + } + ], + "source": [ + "print(df_cont.head())\n", + "print(df_float.head())\n", + "print('categorical dataframe')\n", + "print(df_cat.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We should check for similar special characters in categorical columns and replace them with appropriate values.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.replace(to_replace=[\"?\", \";\"], value=np.nan)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "42588fd11209419b7c81abe23e31a1d2101f60811b02170c268e57ebb57d5d9a" + }, + "kernelspec": { + "display_name": "Python 3.9.7 64-bit ('base': conda)", + "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": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 13f6da2c5543a3837ed1ca52481d0eea392a6b9b Mon Sep 17 00:00:00 2001 From: Sampath N Date: Tue, 14 Dec 2021 15:37:21 +0530 Subject: [PATCH 2/3] data preprocessed --- ml_task.ipynb | 795 ++++++++++++++++++++++++++++++++++++++++-- preprocessed_data.csv | 1 + 2 files changed, 775 insertions(+), 21 deletions(-) create mode 100644 preprocessed_data.csv diff --git a/ml_task.ipynb b/ml_task.ipynb index 6216c13..7477537 100644 --- a/ml_task.ipynb +++ b/ml_task.ipynb @@ -9,12 +9,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", - "import pandas as pd" + "import pandas as pd\n", + "import matplotlib as plt" ] }, { @@ -24,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -38,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -72,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -94,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -108,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -276,7 +277,7 @@ "max 500001.000000 " ] }, - "execution_count": 7, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -292,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -320,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -328,7 +329,7 @@ "output_type": "stream", "text": [ "\n", - "RangeIndex: 20640 entries, 0 to 20639\n", + "Int64Index: 20640 entries, 0 to 20639\n", "Data columns (total 9 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", @@ -342,7 +343,7 @@ " 7 median_income 20640 non-null float64\n", " 8 median_house_value 20640 non-null int64 \n", "dtypes: float64(4), int64(5)\n", - "memory usage: 1.4 MB\n", + "memory usage: 1.6 MB\n", "None\n" ] } @@ -361,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -562,11 +563,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "## Create a dataframe with continuous columns \n", + "df_cont=df.select_dtypes(include=['int64','float64'])\n", "df_int = df.select_dtypes(include = ['int64']) \n", "df_float = df.select_dtypes(include = ['float64'])\n", "## Create a dataframe with categorical columns \n", @@ -575,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -602,7 +604,7 @@ } ], "source": [ - "print(df_cont.head())\n", + "print(df_int.head())\n", "print(df_float.head())\n", "print('categorical dataframe')\n", "print(df_cat.head())" @@ -617,9 +619,214 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
0-122.2337.8841880129.03221268.3252452600
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20640 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -122.23 37.88 41 880 129.0 \n", + "1 -122.22 37.86 21 7099 1106.0 \n", + "2 -122.24 37.85 52 1467 190.0 \n", + "3 -122.25 37.85 52 1274 235.0 \n", + "4 -122.25 37.85 52 1627 280.0 \n", + "... ... ... ... ... ... \n", + "20635 -121.09 39.48 25 1665 374.0 \n", + "20636 -121.21 39.49 18 697 150.0 \n", + "20637 -121.22 39.43 17 2254 485.0 \n", + "20638 -121.32 39.43 18 1860 409.0 \n", + "20639 -121.24 39.37 16 2785 616.0 \n", + "\n", + " population households median_income median_house_value \n", + "0 322 126 8.3252 452600 \n", + "1 2401 1138 8.3014 358500 \n", + "2 496 177 7.2574 352100 \n", + "3 558 219 5.6431 341300 \n", + "4 565 259 3.8462 342200 \n", + "... ... ... ... ... \n", + "20635 845 330 1.5603 78100 \n", + "20636 356 114 2.5568 77100 \n", + "20637 1007 433 1.7000 92300 \n", + "20638 741 349 1.8672 84700 \n", + "20639 1387 530 2.3886 89400 \n", + "\n", + "[20640 rows x 9 columns]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.replace(to_replace=[\"?\", \";\"], value=np.nan)" ] @@ -627,14 +834,560 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "Get the number of missing data points per column and look at the numbvr of missing points in the first ten columns." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "longitude 0\n", + "latitude 0\n", + "housing_median_age 0\n", + "total_rooms 0\n", + "total_bedrooms 207\n", + "population 0\n", + "households 0\n", + "median_income 0\n", + "median_house_value 0\n", + "dtype: int64" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "missing_values_count = df.isnull().sum()\n", + "missing_values_count[0:9]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It might be helpful to see what percentage of the values in our dataset were missing " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "percent missing= 0.11143410852713179\n" + ] + } + ], + "source": [ + "total_cells = np.product(df.shape)\n", + "total_missing = missing_values_count.sum()\n", + "percent_missing = (total_missing/total_cells) * 100\n", + "print('percent missing=',percent_missing)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use the Panda's fillna() function to fill in missing values in a dataframe for us." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
0-122.2337.8841880129.03221268.3252452600
1-122.2237.862170991106.0240111388.3014358500
2-122.2437.85521467190.04961777.2574352100
3-122.2537.85521274235.05582195.6431341300
4-122.2537.85521627280.05652593.8462342200
..............................
20635-121.0939.48251665374.08453301.560378100
20636-121.2139.4918697150.03561142.556877100
20637-121.2239.43172254485.010074331.700092300
20638-121.3239.43181860409.07413491.867284700
20639-121.2439.37162785616.013875302.388689400
\n", + "

20640 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -122.23 37.88 41 880 129.0 \n", + "1 -122.22 37.86 21 7099 1106.0 \n", + "2 -122.24 37.85 52 1467 190.0 \n", + "3 -122.25 37.85 52 1274 235.0 \n", + "4 -122.25 37.85 52 1627 280.0 \n", + "... ... ... ... ... ... \n", + "20635 -121.09 39.48 25 1665 374.0 \n", + "20636 -121.21 39.49 18 697 150.0 \n", + "20637 -121.22 39.43 17 2254 485.0 \n", + "20638 -121.32 39.43 18 1860 409.0 \n", + "20639 -121.24 39.37 16 2785 616.0 \n", + "\n", + " population households median_income median_house_value \n", + "0 322 126 8.3252 452600 \n", + "1 2401 1138 8.3014 358500 \n", + "2 496 177 7.2574 352100 \n", + "3 558 219 5.6431 341300 \n", + "4 565 259 3.8462 342200 \n", + "... ... ... ... ... \n", + "20635 845 330 1.5603 78100 \n", + "20636 356 114 2.5568 77100 \n", + "20637 1007 433 1.7000 92300 \n", + "20638 741 349 1.8672 84700 \n", + "20639 1387 530 2.3886 89400 \n", + "\n", + "[20640 rows x 9 columns]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cont.fillna(537.870553\t)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Percentage of outliers and other descriptive statistical measures" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " outliers outliers% count q1 q3 iqr lower \\\n", + "longitude 0 0.0 20640.0 -121.8 -118.01 3.79 -133.17 \n", + "latitude NaN NaN NaN NaN NaN NaN NaN \n", + "housing_median_age NaN NaN NaN NaN NaN NaN NaN \n", + "total_rooms NaN NaN NaN NaN NaN NaN NaN \n", + "total_bedrooms NaN NaN NaN NaN NaN NaN NaN \n", + "population NaN NaN NaN NaN NaN NaN NaN \n", + "households NaN NaN NaN NaN NaN NaN NaN \n", + "median_income NaN NaN NaN NaN NaN NaN NaN \n", + "median_house_value NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + " upper min max \n", + "longitude -106.64 -124.35 -114.31 \n", + "latitude NaN NaN NaN \n", + "housing_median_age NaN NaN NaN \n", + "total_rooms NaN NaN NaN \n", + "total_bedrooms NaN NaN NaN \n", + "population NaN NaN NaN \n", + "households NaN NaN NaN \n", + "median_income NaN NaN NaN \n", + "median_house_value NaN NaN NaN \n", + " outliers outliers% count q1 q3 iqr lower \\\n", + "longitude 0 0.0 20640.0 -121.80 -118.01 3.79 -133.17 \n", + "latitude 0 0.0 20640.0 33.93 37.71 3.78 22.59 \n", + "housing_median_age NaN NaN NaN NaN NaN NaN NaN \n", + "total_rooms NaN NaN NaN NaN NaN NaN NaN \n", + "total_bedrooms NaN NaN NaN NaN NaN NaN NaN \n", + "population NaN NaN NaN NaN NaN NaN NaN \n", + "households NaN NaN NaN NaN NaN NaN NaN \n", + "median_income NaN NaN NaN NaN NaN NaN NaN \n", + "median_house_value NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + " upper min max \n", + "longitude -106.64 -124.35 -114.31 \n", + "latitude 49.05 32.54 41.95 \n", + "housing_median_age NaN NaN NaN \n", + "total_rooms NaN NaN NaN \n", + "total_bedrooms NaN NaN NaN \n", + "population NaN NaN NaN \n", + "households NaN NaN NaN \n", + "median_income NaN NaN NaN \n", + "median_house_value NaN NaN NaN \n", + " outliers outliers% count q1 q3 iqr lower \\\n", + "longitude 0 0.0 20640.0 -121.80 -118.01 3.79 -133.17 \n", + "latitude 0 0.0 20640.0 33.93 37.71 3.78 22.59 \n", + "housing_median_age 0 0.0 20640.0 18.00 37.00 19.00 -39.00 \n", + "total_rooms NaN NaN NaN NaN NaN NaN NaN \n", + "total_bedrooms NaN NaN NaN NaN NaN NaN NaN \n", + "population NaN NaN NaN NaN NaN NaN NaN \n", + "households NaN NaN NaN NaN NaN NaN NaN \n", + "median_income NaN NaN NaN NaN NaN NaN NaN \n", + "median_house_value NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + " upper min max \n", + "longitude -106.64 -124.35 -114.31 \n", + "latitude 49.05 32.54 41.95 \n", + "housing_median_age 94.00 1.00 52.00 \n", + "total_rooms NaN NaN NaN \n", + "total_bedrooms NaN NaN NaN \n", + "population NaN NaN NaN \n", + "households NaN NaN NaN \n", + "median_income NaN NaN NaN \n", + "median_house_value NaN NaN NaN \n", + " outliers outliers% count q1 q3 iqr \\\n", + "longitude 0 0.0 20640.0 -121.80 -118.01 3.79 \n", + "latitude 0 0.0 20640.0 33.93 37.71 3.78 \n", + "housing_median_age 0 0.0 20640.0 18.00 37.00 19.00 \n", + "total_rooms 494 2.0 20640.0 1447.75 3148.00 1700.25 \n", + "total_bedrooms NaN NaN NaN NaN NaN NaN \n", + "population NaN NaN NaN NaN NaN NaN \n", + "households NaN NaN NaN NaN NaN NaN \n", + "median_income NaN NaN NaN NaN NaN NaN \n", + "median_house_value NaN NaN NaN NaN NaN NaN \n", + "\n", + " lower upper min max \n", + "longitude -133.17 -106.64 -124.35 -114.31 \n", + "latitude 22.59 49.05 32.54 41.95 \n", + "housing_median_age -39.00 94.00 1.00 52.00 \n", + "total_rooms -3653.00 8248.75 2.00 39320.00 \n", + "total_bedrooms NaN NaN NaN NaN \n", + "population NaN NaN NaN NaN \n", + "households NaN NaN NaN NaN \n", + "median_income NaN NaN NaN NaN \n", + "median_house_value NaN NaN NaN NaN \n", + " outliers outliers% count q1 q3 iqr \\\n", + "longitude 0 0.0 20640.0 -121.80 -118.01 3.79 \n", + "latitude 0 0.0 20640.0 33.93 37.71 3.78 \n", + "housing_median_age 0 0.0 20640.0 18.00 37.00 19.00 \n", + "total_rooms 494 2.0 20640.0 1447.75 3148.00 1700.25 \n", + "total_bedrooms 443 2.0 20640.0 296.00 647.00 351.00 \n", + "population NaN NaN NaN NaN NaN NaN \n", + "households NaN NaN NaN NaN NaN NaN \n", + "median_income NaN NaN NaN NaN NaN NaN \n", + "median_house_value NaN NaN NaN NaN NaN NaN \n", + "\n", + " lower upper min max \n", + "longitude -133.17 -106.64 -124.35 -114.31 \n", + "latitude 22.59 49.05 32.54 41.95 \n", + "housing_median_age -39.00 94.00 1.00 52.00 \n", + "total_rooms -3653.00 8248.75 2.00 39320.00 \n", + "total_bedrooms -757.00 1700.00 1.00 6445.00 \n", + "population NaN NaN NaN NaN \n", + "households NaN NaN NaN NaN \n", + "median_income NaN NaN NaN NaN \n", + "median_house_value NaN NaN NaN NaN \n", + " outliers outliers% count q1 q3 iqr \\\n", + "longitude 0 0.0 20640.0 -121.80 -118.01 3.79 \n", + "latitude 0 0.0 20640.0 33.93 37.71 3.78 \n", + "housing_median_age 0 0.0 20640.0 18.00 37.00 19.00 \n", + "total_rooms 494 2.0 20640.0 1447.75 3148.00 1700.25 \n", + "total_bedrooms 443 2.0 20640.0 296.00 647.00 351.00 \n", + "population 421 2.0 20640.0 787.00 1725.00 938.00 \n", + "households NaN NaN NaN NaN NaN NaN \n", + "median_income NaN NaN NaN NaN NaN NaN \n", + "median_house_value NaN NaN NaN NaN NaN NaN \n", + "\n", + " lower upper min max \n", + "longitude -133.17 -106.64 -124.35 -114.31 \n", + "latitude 22.59 49.05 32.54 41.95 \n", + "housing_median_age -39.00 94.00 1.00 52.00 \n", + "total_rooms -3653.00 8248.75 2.00 39320.00 \n", + "total_bedrooms -757.00 1700.00 1.00 6445.00 \n", + "population -2027.00 4539.00 3.00 35682.00 \n", + "households NaN NaN NaN NaN \n", + "median_income NaN NaN NaN NaN \n", + "median_house_value NaN NaN NaN NaN \n", + " outliers outliers% count q1 q3 iqr \\\n", + "longitude 0 0.0 20640.0 -121.80 -118.01 3.79 \n", + "latitude 0 0.0 20640.0 33.93 37.71 3.78 \n", + "housing_median_age 0 0.0 20640.0 18.00 37.00 19.00 \n", + "total_rooms 494 2.0 20640.0 1447.75 3148.00 1700.25 \n", + "total_bedrooms 443 2.0 20640.0 296.00 647.00 351.00 \n", + "population 421 2.0 20640.0 787.00 1725.00 938.00 \n", + "households 415 2.0 20640.0 280.00 605.00 325.00 \n", + "median_income NaN NaN NaN NaN NaN NaN \n", + "median_house_value NaN NaN NaN NaN NaN NaN \n", + "\n", + " lower upper min max \n", + "longitude -133.17 -106.64 -124.35 -114.31 \n", + "latitude 22.59 49.05 32.54 41.95 \n", + "housing_median_age -39.00 94.00 1.00 52.00 \n", + "total_rooms -3653.00 8248.75 2.00 39320.00 \n", + "total_bedrooms -757.00 1700.00 1.00 6445.00 \n", + "population -2027.00 4539.00 3.00 35682.00 \n", + "households -695.00 1580.00 1.00 6082.00 \n", + "median_income NaN NaN NaN NaN \n", + "median_house_value NaN NaN NaN NaN \n", + " outliers outliers% count q1 q3 \\\n", + "longitude 0 0.0 20640.0 -121.8000 -118.01000 \n", + "latitude 0 0.0 20640.0 33.9300 37.71000 \n", + "housing_median_age 0 0.0 20640.0 18.0000 37.00000 \n", + "total_rooms 494 2.0 20640.0 1447.7500 3148.00000 \n", + "total_bedrooms 443 2.0 20640.0 296.0000 647.00000 \n", + "population 421 2.0 20640.0 787.0000 1725.00000 \n", + "households 415 2.0 20640.0 280.0000 605.00000 \n", + "median_income 140 1.0 20640.0 2.5634 4.74325 \n", + "median_house_value NaN NaN NaN NaN NaN \n", + "\n", + " iqr lower upper min max \n", + "longitude 3.79000 -133.17000 -106.6400 -124.3500 -114.3100 \n", + "latitude 3.78000 22.59000 49.0500 32.5400 41.9500 \n", + "housing_median_age 19.00000 -39.00000 94.0000 1.0000 52.0000 \n", + "total_rooms 1700.25000 -3653.00000 8248.7500 2.0000 39320.0000 \n", + "total_bedrooms 351.00000 -757.00000 1700.0000 1.0000 6445.0000 \n", + "population 938.00000 -2027.00000 4539.0000 3.0000 35682.0000 \n", + "households 325.00000 -695.00000 1580.0000 1.0000 6082.0000 \n", + "median_income 2.17985 -3.97615 11.2828 0.4999 15.0001 \n", + "median_house_value NaN NaN NaN NaN NaN \n", + " outliers outliers% count q1 q3 \\\n", + "longitude 0 0.0 20640.0 -121.8000 -118.01000 \n", + "latitude 0 0.0 20640.0 33.9300 37.71000 \n", + "housing_median_age 0 0.0 20640.0 18.0000 37.00000 \n", + "total_rooms 494 2.0 20640.0 1447.7500 3148.00000 \n", + "total_bedrooms 443 2.0 20640.0 296.0000 647.00000 \n", + "population 421 2.0 20640.0 787.0000 1725.00000 \n", + "households 415 2.0 20640.0 280.0000 605.00000 \n", + "median_income 140 1.0 20640.0 2.5634 4.74325 \n", + "median_house_value 0 0.0 20640.0 119600.0000 264725.00000 \n", + "\n", + " iqr lower upper min \\\n", + "longitude 3.79000 -133.17000 -106.6400 -124.3500 \n", + "latitude 3.78000 22.59000 49.0500 32.5400 \n", + "housing_median_age 19.00000 -39.00000 94.0000 1.0000 \n", + "total_rooms 1700.25000 -3653.00000 8248.7500 2.0000 \n", + "total_bedrooms 351.00000 -757.00000 1700.0000 1.0000 \n", + "population 938.00000 -2027.00000 4539.0000 3.0000 \n", + "households 325.00000 -695.00000 1580.0000 1.0000 \n", + "median_income 2.17985 -3.97615 11.2828 0.4999 \n", + "median_house_value 145125.00000 -315775.00000 700100.0000 14999.0000 \n", + "\n", + " max \n", + "longitude -114.3100 \n", + "latitude 41.9500 \n", + "housing_median_age 52.0000 \n", + "total_rooms 39320.0000 \n", + "total_bedrooms 6445.0000 \n", + "population 35682.0000 \n", + "households 6082.0000 \n", + "median_income 15.0001 \n", + "median_house_value 500001.0000 \n" + ] + } + ], + "source": [ + "\n", + "df_outliers = pd.DataFrame(index=df_cont.columns, columns=['outliers', 'outliers%']) \n", + "for col in df_cont.columns:\n", + " if any(x in str(df_cont[col].dtype) for x in ['int', 'float']):\n", + " df_outliers.loc[col, 'count'] = len(df_cont)\n", + " df_outliers.loc[col, 'q1'] = df_cont[col].quantile(0.25)\n", + " df_outliers.loc[col, 'q3'] = df_cont[col].quantile(0.75)\n", + " df_outliers.loc[col, 'iqr'] = df_outliers.loc[col, 'q3'] - df_outliers.loc[col, 'q1']\n", + " df_outliers.loc[col, 'lower'] = df_outliers.loc[col, 'q1'] - (3 * df_outliers.loc[col, 'iqr'])\n", + " df_outliers.loc[col, 'upper'] = df_outliers.loc[col, 'q3'] + (3 * df_outliers.loc[col, 'iqr'])\n", + " df_outliers.loc[col, 'min'] = df[col].min()\n", + " df_outliers.loc[col, 'max'] = df[col].max()\n", + " df_outliers.loc[col, 'outliers'] = ((df_cont[col] < df_outliers.loc[col, 'lower']) | (df[col] > df_outliers.loc[col,'upper'])).sum()\n", + " df_outliers.loc[col, 'outliers%'] = np.round(df_outliers.loc[col,'outliers'] / len(df_cont) *100)\n", + " print(df_outliers)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Treating outliers" + ] + }, + { + "cell_type": "code", + "execution_count": 53, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "from scipy import stats as st\n", + "z_scores = st.zscore(df_cont)\n", + "abs_z_scores = np.abs(z_scores)\n", + "entries = (abs_z_scores < 3).all(axis=1)\n", + "df_cont = df_cont[entries]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Saving preprocessed dataframe in a csv " + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "df_cont.to_csv('preprocessed_data.csv')" + ] } ], "metadata": { diff --git a/preprocessed_data.csv b/preprocessed_data.csv new file mode 100644 index 0000000..5e4c6c9 --- /dev/null +++ b/preprocessed_data.csv @@ -0,0 +1 @@ +,longitude,latitude,housing_median_age,total_rooms,total_bedrooms,population,households,median_income,median_house_value From aff5a884cec4d80043595d54b377990ae9177adc Mon Sep 17 00:00:00 2001 From: Sampath N Date: Mon, 10 Jan 2022 13:27:49 +0530 Subject: [PATCH 3/3] final --- ml_task.ipynb | 586 +- preprocessed_data.csv | 20433 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 20639 insertions(+), 380 deletions(-) diff --git a/ml_task.ipynb b/ml_task.ipynb index 7477537..e9e2cd1 100644 --- a/ml_task.ipynb +++ b/ml_task.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -18,28 +18,18 @@ "import matplotlib as plt" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "df=pd.read_csv('dataset_housingprices.csv')" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -66,14 +56,9 @@ "print(df.head())" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -88,28 +73,18 @@ "print(df.shape)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "df.drop_duplicates(inplace=True)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -277,7 +252,7 @@ "max 500001.000000 " ] }, - "execution_count": 34, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -286,14 +261,9 @@ "df.describe()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -321,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -362,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -563,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -577,19 +547,27 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " housing_median_age total_rooms population households median_house_value\n", - "0 41 880 322 126 452600\n", - "1 21 7099 2401 1138 358500\n", - "2 52 1467 496 177 352100\n", - "3 52 1274 558 219 341300\n", - "4 52 1627 565 259 342200\n", + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -122.23 37.88 41 880 129.0 \n", + "1 -122.22 37.86 21 7099 1106.0 \n", + "2 -122.24 37.85 52 1467 190.0 \n", + "3 -122.25 37.85 52 1274 235.0 \n", + "4 -122.25 37.85 52 1627 280.0 \n", + "\n", + " population households median_income median_house_value \n", + "0 322 126 8.3252 452600 \n", + "1 2401 1138 8.3014 358500 \n", + "2 496 177 7.2574 352100 \n", + "3 558 219 5.6431 341300 \n", + "4 565 259 3.8462 342200 \n", + "next\n", " longitude latitude total_bedrooms median_income\n", "0 -122.23 37.88 129.0 8.3252\n", "1 -122.22 37.86 1106.0 8.3014\n", @@ -604,7 +582,8 @@ } ], "source": [ - "print(df_int.head())\n", + "print(df_cont.head())\n", + "print('next')\n", "print(df_float.head())\n", "print('categorical dataframe')\n", "print(df_cat.head())" @@ -619,7 +598,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -822,7 +801,7 @@ "[20640 rows x 9 columns]" ] }, - "execution_count": 40, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -840,7 +819,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -858,7 +837,7 @@ "dtype: int64" ] }, - "execution_count": 41, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -877,7 +856,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -904,7 +883,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -1000,120 +979,171 @@ " 3.8462\n", " 342200\n", " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " \n", + "\n", + "" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -122.23 37.88 41 880 129.0 \n", + "1 -122.22 37.86 21 7099 1106.0 \n", + "2 -122.24 37.85 52 1467 190.0 \n", + "3 -122.25 37.85 52 1274 235.0 \n", + "4 -122.25 37.85 52 1627 280.0 \n", + "\n", + " population households median_income median_house_value \n", + "0 322 126 8.3252 452600 \n", + "1 2401 1138 8.3014 358500 \n", + "2 496 177 7.2574 352100 \n", + "3 558 219 5.6431 341300 \n", + "4 565 259 3.8462 342200 " + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cont.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "df_cont.fillna(537.87055)\n", + "df_cont.replace([np.inf,-np.inf],np.nan,inplace=True)\n", + "df_cont.dropna(inplace=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
20635-121.0939.48251665374.08453301.5603781000-122.2337.8841880129.03221268.3252452600
20636-121.2139.4918697150.03561142.5568771001-122.2237.862170991106.0240111388.3014358500
20637-121.2239.43172254485.010074331.7000923002-122.2437.85521467190.04961777.2574352100
20638-121.3239.43181860409.07413491.8672847003-122.2537.85521274235.05582195.6431341300
20639-121.2439.37162785616.013875302.3886894004-122.2537.85521627280.05652593.8462342200
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20640 rows × 9 columns

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" ], "text/plain": [ - " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", - "0 -122.23 37.88 41 880 129.0 \n", - "1 -122.22 37.86 21 7099 1106.0 \n", - "2 -122.24 37.85 52 1467 190.0 \n", - "3 -122.25 37.85 52 1274 235.0 \n", - "4 -122.25 37.85 52 1627 280.0 \n", - "... ... ... ... ... ... \n", - "20635 -121.09 39.48 25 1665 374.0 \n", - "20636 -121.21 39.49 18 697 150.0 \n", - "20637 -121.22 39.43 17 2254 485.0 \n", - "20638 -121.32 39.43 18 1860 409.0 \n", - "20639 -121.24 39.37 16 2785 616.0 \n", - "\n", - " population households median_income median_house_value \n", - "0 322 126 8.3252 452600 \n", - "1 2401 1138 8.3014 358500 \n", - "2 496 177 7.2574 352100 \n", - "3 558 219 5.6431 341300 \n", - "4 565 259 3.8462 342200 \n", - "... ... ... ... ... \n", - "20635 845 330 1.5603 78100 \n", - "20636 356 114 2.5568 77100 \n", - "20637 1007 433 1.7000 92300 \n", - "20638 741 349 1.8672 84700 \n", - "20639 1387 530 2.3886 89400 \n", + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -122.23 37.88 41 880 129.0 \n", + "1 -122.22 37.86 21 7099 1106.0 \n", + "2 -122.24 37.85 52 1467 190.0 \n", + "3 -122.25 37.85 52 1274 235.0 \n", + "4 -122.25 37.85 52 1627 280.0 \n", "\n", - "[20640 rows x 9 columns]" + " population households median_income median_house_value \n", + "0 322 126 8.3252 452600 \n", + "1 2401 1138 8.3014 358500 \n", + "2 496 177 7.2574 352100 \n", + "3 558 219 5.6431 341300 \n", + "4 565 259 3.8462 342200 " ] }, - "execution_count": 48, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_cont.fillna(537.870553\t)" + "df_cont.head()" ] }, { @@ -1123,270 +1153,66 @@ "Percentage of outliers and other descriptive statistical measures" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Treating outliers" + ] + }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 80, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " outliers outliers% count q1 q3 iqr lower \\\n", - "longitude 0 0.0 20640.0 -121.8 -118.01 3.79 -133.17 \n", - "latitude NaN NaN NaN NaN NaN NaN NaN \n", - "housing_median_age NaN NaN NaN NaN NaN NaN NaN \n", - "total_rooms NaN NaN NaN NaN NaN NaN NaN \n", - "total_bedrooms NaN NaN NaN NaN NaN NaN NaN \n", - "population NaN NaN NaN NaN NaN NaN NaN \n", - "households NaN NaN NaN NaN NaN NaN NaN \n", - "median_income NaN NaN NaN NaN NaN NaN NaN \n", - "median_house_value NaN NaN NaN NaN NaN NaN NaN \n", - "\n", - " upper min max \n", - "longitude -106.64 -124.35 -114.31 \n", - "latitude NaN NaN NaN \n", - "housing_median_age NaN NaN NaN \n", - "total_rooms NaN NaN NaN \n", - "total_bedrooms NaN NaN NaN \n", - "population NaN NaN NaN \n", - "households NaN NaN NaN \n", - "median_income NaN NaN NaN \n", - "median_house_value NaN NaN NaN \n", - " outliers outliers% count q1 q3 iqr lower \\\n", - "longitude 0 0.0 20640.0 -121.80 -118.01 3.79 -133.17 \n", - "latitude 0 0.0 20640.0 33.93 37.71 3.78 22.59 \n", - "housing_median_age NaN NaN NaN NaN NaN NaN NaN \n", - "total_rooms NaN NaN NaN NaN NaN NaN NaN \n", - "total_bedrooms NaN NaN NaN NaN NaN NaN NaN \n", - "population NaN NaN NaN NaN NaN NaN NaN \n", - "households NaN NaN NaN NaN NaN NaN NaN \n", - "median_income NaN NaN NaN NaN NaN NaN NaN \n", - "median_house_value NaN NaN NaN NaN NaN NaN NaN \n", - "\n", - " upper min max \n", - "longitude -106.64 -124.35 -114.31 \n", - "latitude 49.05 32.54 41.95 \n", - "housing_median_age NaN NaN NaN \n", - "total_rooms NaN NaN NaN \n", - "total_bedrooms NaN NaN NaN \n", - "population NaN NaN NaN \n", - "households NaN NaN NaN \n", - "median_income NaN NaN NaN \n", - "median_house_value NaN NaN NaN \n", - " outliers outliers% count q1 q3 iqr lower \\\n", - "longitude 0 0.0 20640.0 -121.80 -118.01 3.79 -133.17 \n", - "latitude 0 0.0 20640.0 33.93 37.71 3.78 22.59 \n", - "housing_median_age 0 0.0 20640.0 18.00 37.00 19.00 -39.00 \n", - "total_rooms NaN NaN NaN NaN NaN NaN NaN \n", - "total_bedrooms NaN NaN NaN NaN NaN NaN NaN \n", - "population NaN NaN NaN NaN NaN NaN NaN \n", - "households NaN NaN NaN NaN NaN NaN NaN \n", - "median_income NaN NaN NaN NaN NaN NaN NaN \n", - "median_house_value NaN NaN NaN NaN NaN NaN NaN \n", - "\n", - " upper min max \n", - "longitude -106.64 -124.35 -114.31 \n", - "latitude 49.05 32.54 41.95 \n", - "housing_median_age 94.00 1.00 52.00 \n", - "total_rooms NaN NaN NaN \n", - "total_bedrooms NaN NaN NaN \n", - "population NaN NaN NaN \n", - "households NaN NaN NaN \n", - "median_income NaN NaN NaN \n", - "median_house_value NaN NaN NaN \n", - " outliers outliers% count q1 q3 iqr \\\n", - "longitude 0 0.0 20640.0 -121.80 -118.01 3.79 \n", - "latitude 0 0.0 20640.0 33.93 37.71 3.78 \n", - "housing_median_age 0 0.0 20640.0 18.00 37.00 19.00 \n", - "total_rooms 494 2.0 20640.0 1447.75 3148.00 1700.25 \n", - "total_bedrooms NaN NaN NaN NaN NaN NaN \n", - "population NaN NaN NaN NaN NaN NaN \n", - "households NaN NaN NaN NaN NaN NaN \n", - "median_income NaN NaN NaN NaN NaN NaN \n", - "median_house_value NaN NaN NaN NaN NaN NaN \n", - "\n", - " lower upper min max \n", - "longitude -133.17 -106.64 -124.35 -114.31 \n", - "latitude 22.59 49.05 32.54 41.95 \n", - "housing_median_age -39.00 94.00 1.00 52.00 \n", - "total_rooms -3653.00 8248.75 2.00 39320.00 \n", - "total_bedrooms NaN NaN NaN NaN \n", - "population NaN NaN NaN NaN \n", - "households NaN NaN NaN NaN \n", - "median_income NaN NaN NaN NaN \n", - "median_house_value NaN NaN NaN NaN \n", - " outliers outliers% count q1 q3 iqr \\\n", - "longitude 0 0.0 20640.0 -121.80 -118.01 3.79 \n", - "latitude 0 0.0 20640.0 33.93 37.71 3.78 \n", - "housing_median_age 0 0.0 20640.0 18.00 37.00 19.00 \n", - "total_rooms 494 2.0 20640.0 1447.75 3148.00 1700.25 \n", - "total_bedrooms 443 2.0 20640.0 296.00 647.00 351.00 \n", - "population NaN NaN NaN NaN NaN NaN \n", - "households NaN NaN NaN NaN NaN NaN \n", - "median_income NaN NaN NaN NaN NaN NaN \n", - "median_house_value NaN NaN NaN NaN NaN NaN \n", - "\n", - " lower upper min max \n", - "longitude -133.17 -106.64 -124.35 -114.31 \n", - "latitude 22.59 49.05 32.54 41.95 \n", - "housing_median_age -39.00 94.00 1.00 52.00 \n", - "total_rooms -3653.00 8248.75 2.00 39320.00 \n", - "total_bedrooms -757.00 1700.00 1.00 6445.00 \n", - "population NaN NaN NaN NaN \n", - "households NaN NaN NaN NaN \n", - "median_income NaN NaN NaN NaN \n", - "median_house_value NaN NaN NaN NaN \n", - " outliers outliers% count q1 q3 iqr \\\n", - "longitude 0 0.0 20640.0 -121.80 -118.01 3.79 \n", - "latitude 0 0.0 20640.0 33.93 37.71 3.78 \n", - "housing_median_age 0 0.0 20640.0 18.00 37.00 19.00 \n", - "total_rooms 494 2.0 20640.0 1447.75 3148.00 1700.25 \n", - "total_bedrooms 443 2.0 20640.0 296.00 647.00 351.00 \n", - "population 421 2.0 20640.0 787.00 1725.00 938.00 \n", - "households NaN NaN NaN NaN NaN NaN \n", - "median_income NaN NaN NaN NaN NaN NaN \n", - "median_house_value NaN NaN NaN NaN NaN NaN \n", - "\n", - " lower upper min max \n", - "longitude -133.17 -106.64 -124.35 -114.31 \n", - "latitude 22.59 49.05 32.54 41.95 \n", - "housing_median_age -39.00 94.00 1.00 52.00 \n", - "total_rooms -3653.00 8248.75 2.00 39320.00 \n", - "total_bedrooms -757.00 1700.00 1.00 6445.00 \n", - "population -2027.00 4539.00 3.00 35682.00 \n", - "households NaN NaN NaN NaN \n", - "median_income NaN NaN NaN NaN \n", - "median_house_value NaN NaN NaN NaN \n", - " outliers outliers% count q1 q3 iqr \\\n", - "longitude 0 0.0 20640.0 -121.80 -118.01 3.79 \n", - "latitude 0 0.0 20640.0 33.93 37.71 3.78 \n", - "housing_median_age 0 0.0 20640.0 18.00 37.00 19.00 \n", - "total_rooms 494 2.0 20640.0 1447.75 3148.00 1700.25 \n", - "total_bedrooms 443 2.0 20640.0 296.00 647.00 351.00 \n", - "population 421 2.0 20640.0 787.00 1725.00 938.00 \n", - "households 415 2.0 20640.0 280.00 605.00 325.00 \n", - "median_income NaN NaN NaN NaN NaN NaN \n", - "median_house_value NaN NaN NaN NaN NaN NaN \n", - "\n", - " lower upper min max \n", - "longitude -133.17 -106.64 -124.35 -114.31 \n", - "latitude 22.59 49.05 32.54 41.95 \n", - "housing_median_age -39.00 94.00 1.00 52.00 \n", - "total_rooms -3653.00 8248.75 2.00 39320.00 \n", - "total_bedrooms -757.00 1700.00 1.00 6445.00 \n", - "population -2027.00 4539.00 3.00 35682.00 \n", - "households -695.00 1580.00 1.00 6082.00 \n", - "median_income NaN NaN NaN NaN \n", - "median_house_value NaN NaN NaN NaN \n", - " outliers outliers% count q1 q3 \\\n", - "longitude 0 0.0 20640.0 -121.8000 -118.01000 \n", - "latitude 0 0.0 20640.0 33.9300 37.71000 \n", - "housing_median_age 0 0.0 20640.0 18.0000 37.00000 \n", - "total_rooms 494 2.0 20640.0 1447.7500 3148.00000 \n", - "total_bedrooms 443 2.0 20640.0 296.0000 647.00000 \n", - "population 421 2.0 20640.0 787.0000 1725.00000 \n", - "households 415 2.0 20640.0 280.0000 605.00000 \n", - "median_income 140 1.0 20640.0 2.5634 4.74325 \n", - "median_house_value NaN NaN NaN NaN NaN \n", - "\n", - " iqr lower upper min max \n", - "longitude 3.79000 -133.17000 -106.6400 -124.3500 -114.3100 \n", - "latitude 3.78000 22.59000 49.0500 32.5400 41.9500 \n", - "housing_median_age 19.00000 -39.00000 94.0000 1.0000 52.0000 \n", - "total_rooms 1700.25000 -3653.00000 8248.7500 2.0000 39320.0000 \n", - "total_bedrooms 351.00000 -757.00000 1700.0000 1.0000 6445.0000 \n", - "population 938.00000 -2027.00000 4539.0000 3.0000 35682.0000 \n", - "households 325.00000 -695.00000 1580.0000 1.0000 6082.0000 \n", - "median_income 2.17985 -3.97615 11.2828 0.4999 15.0001 \n", - "median_house_value NaN NaN NaN NaN NaN \n", - " outliers outliers% count q1 q3 \\\n", - "longitude 0 0.0 20640.0 -121.8000 -118.01000 \n", - "latitude 0 0.0 20640.0 33.9300 37.71000 \n", - "housing_median_age 0 0.0 20640.0 18.0000 37.00000 \n", - "total_rooms 494 2.0 20640.0 1447.7500 3148.00000 \n", - "total_bedrooms 443 2.0 20640.0 296.0000 647.00000 \n", - "population 421 2.0 20640.0 787.0000 1725.00000 \n", - "households 415 2.0 20640.0 280.0000 605.00000 \n", - "median_income 140 1.0 20640.0 2.5634 4.74325 \n", - "median_house_value 0 0.0 20640.0 119600.0000 264725.00000 \n", - "\n", - " iqr lower upper min \\\n", - "longitude 3.79000 -133.17000 -106.6400 -124.3500 \n", - "latitude 3.78000 22.59000 49.0500 32.5400 \n", - "housing_median_age 19.00000 -39.00000 94.0000 1.0000 \n", - "total_rooms 1700.25000 -3653.00000 8248.7500 2.0000 \n", - "total_bedrooms 351.00000 -757.00000 1700.0000 1.0000 \n", - "population 938.00000 -2027.00000 4539.0000 3.0000 \n", - "households 325.00000 -695.00000 1580.0000 1.0000 \n", - "median_income 2.17985 -3.97615 11.2828 0.4999 \n", - "median_house_value 145125.00000 -315775.00000 700100.0000 14999.0000 \n", - "\n", - " max \n", - "longitude -114.3100 \n", - "latitude 41.9500 \n", - "housing_median_age 52.0000 \n", - "total_rooms 39320.0000 \n", - "total_bedrooms 6445.0000 \n", - "population 35682.0000 \n", - "households 6082.0000 \n", - "median_income 15.0001 \n", - "median_house_value 500001.0000 \n" - ] - } - ], + "outputs": [], "source": [ - "\n", - "df_outliers = pd.DataFrame(index=df_cont.columns, columns=['outliers', 'outliers%']) \n", - "for col in df_cont.columns:\n", - " if any(x in str(df_cont[col].dtype) for x in ['int', 'float']):\n", - " df_outliers.loc[col, 'count'] = len(df_cont)\n", - " df_outliers.loc[col, 'q1'] = df_cont[col].quantile(0.25)\n", - " df_outliers.loc[col, 'q3'] = df_cont[col].quantile(0.75)\n", - " df_outliers.loc[col, 'iqr'] = df_outliers.loc[col, 'q3'] - df_outliers.loc[col, 'q1']\n", - " df_outliers.loc[col, 'lower'] = df_outliers.loc[col, 'q1'] - (3 * df_outliers.loc[col, 'iqr'])\n", - " df_outliers.loc[col, 'upper'] = df_outliers.loc[col, 'q3'] + (3 * df_outliers.loc[col, 'iqr'])\n", - " df_outliers.loc[col, 'min'] = df[col].min()\n", - " df_outliers.loc[col, 'max'] = df[col].max()\n", - " df_outliers.loc[col, 'outliers'] = ((df_cont[col] < df_outliers.loc[col, 'lower']) | (df[col] > df_outliers.loc[col,'upper'])).sum()\n", - " df_outliers.loc[col, 'outliers%'] = np.round(df_outliers.loc[col,'outliers'] / len(df_cont) *100)\n", - " print(df_outliers)" + "df_cont.to_csv('preprocessed_data.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Treating outliers" + "LINEAR REGRESSION MODEL TO PREDICT median_house_value" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ - "from scipy import stats as st\n", - "z_scores = st.zscore(df_cont)\n", - "abs_z_scores = np.abs(z_scores)\n", - "entries = (abs_z_scores < 3).all(axis=1)\n", - "df_cont = df_cont[entries]" + "from sklearn.linear_model import LinearRegression" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 82, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(20433, 8)\n", + "(20433,)\n" + ] + } + ], "source": [ - "Saving preprocessed dataframe in a csv " + "x=df_cont[['longitude','latitude','housing_median_age','total_rooms','total_bedrooms','population','households','median_income']].values\n", + "print(x.shape)\n", + "y=df_cont['median_house_value'].values\n", + "print(y.shape)\n" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ - "df_cont.to_csv('preprocessed_data.csv')" + "model=LinearRegression().fit(x,y)\n" ] } ], diff --git a/preprocessed_data.csv b/preprocessed_data.csv index 5e4c6c9..2d06a7f 100644 --- a/preprocessed_data.csv +++ b/preprocessed_data.csv @@ -1 +1,20434 @@ ,longitude,latitude,housing_median_age,total_rooms,total_bedrooms,population,households,median_income,median_house_value +0,-122.23,37.88,41,880,129.0,322,126,8.3252,452600 +1,-122.22,37.86,21,7099,1106.0,2401,1138,8.3014,358500 +2,-122.24,37.85,52,1467,190.0,496,177,7.2574,352100 +3,-122.25,37.85,52,1274,235.0,558,219,5.6431,341300 +4,-122.25,37.85,52,1627,280.0,565,259,3.8462,342200 +5,-122.25,37.85,52,919,213.0,413,193,4.0368,269700 +6,-122.25,37.84,52,2535,489.0,1094,514,3.6591,299200 +7,-122.25,37.84,52,3104,687.0,1157,647,3.12,241400 +8,-122.26,37.84,42,2555,665.0,1206,595,2.0804,226700 +9,-122.25,37.84,52,3549,707.0,1551,714,3.6912,261100 +10,-122.26,37.85,52,2202,434.0,910,402,3.2031,281500 +11,-122.26,37.85,52,3503,752.0,1504,734,3.2705,241800 +12,-122.26,37.85,52,2491,474.0,1098,468,3.075,213500 +13,-122.26,37.84,52,696,191.0,345,174,2.6736,191300 +14,-122.26,37.85,52,2643,626.0,1212,620,1.9167,159200 +15,-122.26,37.85,50,1120,283.0,697,264,2.125,140000 +16,-122.27,37.85,52,1966,347.0,793,331,2.775,152500 +17,-122.27,37.85,52,1228,293.0,648,303,2.1202,155500 +18,-122.26,37.84,50,2239,455.0,990,419,1.9911,158700 +19,-122.27,37.84,52,1503,298.0,690,275,2.6033,162900 +20,-122.27,37.85,40,751,184.0,409,166,1.3578,147500 +21,-122.27,37.85,42,1639,367.0,929,366,1.7135,159800 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