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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "I76BhvxwrMfn",
"colab_type": "text"
},
"source": [
"# PyCaret 2 House Price Prediction Example"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wSSQFNvlrMfp",
"colab_type": "text"
},
"source": [
"This notebook is created using PyCaret 2.0. Last updated : 04-08-2020"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "gobl5HA-juwP"
},
"source": [
"House Price Prediction data set from Kaggle https://www.kaggle.com/c/house-prices-advanced-regression-techniques
\n",
"Train Dataset consists of 1460 Samples with 81 features including the SalePrice
\n",
"Test Dataset consists of 1459 Samples wit 80 features"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "ICXiqtC_TacA",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "49a1f1f6-c4e7-499a-eb34-4d19bceb4562"
},
"source": [
"# Mount Google Drive \n",
"# Skip this step if using on local hardware \n",
"from google.colab import drive\n",
"drive.mount('/content/gdrive')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "pD-STV05yakD",
"colab": {}
},
"source": [
"# Works with pycaret and pycaret 2\n",
"#!pip install pycaret==2.0\n",
"from pycaret.regression import *\n",
"import pandas as pd"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "y0qd3_m7rMfu",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "eff3c677-6bf6-4e21-de39-2c1e901254ce"
},
"source": [
"# check version\n",
"from pycaret.utils import version\n",
"version()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"2.0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "IVl3sVtl32im",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "5c427692-f3ce-400f-a5ac-ae916ad6d11c"
},
"source": [
"# Chane path as per your file structure\n",
"# Remove root_path if using local hardware\n",
"\n",
"root_path = 'gdrive/My Drive/Colab Notebooks/'\n",
"\n",
"data = pd.read_csv('gdrive/My Drive/Colab Notebooks/HousePrice/train.csv')\n",
"\n",
"test_data = pd.read_csv('gdrive/My Drive/Colab Notebooks/HousePrice/test.csv')\n",
"\n",
"print(data.shape, test_data.shape)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"(1460, 81) (1459, 80)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "kpCdQOa9ZZnH",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 256
},
"outputId": "7faadb2f-8e76-4a34-a85e-e24f285a5113"
},
"source": [
"data.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Id | \n",
" MSSubClass | \n",
" MSZoning | \n",
" LotFrontage | \n",
" LotArea | \n",
" Street | \n",
" Alley | \n",
" LotShape | \n",
" LandContour | \n",
" Utilities | \n",
" LotConfig | \n",
" LandSlope | \n",
" Neighborhood | \n",
" Condition1 | \n",
" Condition2 | \n",
" BldgType | \n",
" HouseStyle | \n",
" OverallQual | \n",
" OverallCond | \n",
" YearBuilt | \n",
" YearRemodAdd | \n",
" RoofStyle | \n",
" RoofMatl | \n",
" Exterior1st | \n",
" Exterior2nd | \n",
" MasVnrType | \n",
" MasVnrArea | \n",
" ExterQual | \n",
" ExterCond | \n",
" Foundation | \n",
" BsmtQual | \n",
" BsmtCond | \n",
" BsmtExposure | \n",
" BsmtFinType1 | \n",
" BsmtFinSF1 | \n",
" BsmtFinType2 | \n",
" BsmtFinSF2 | \n",
" BsmtUnfSF | \n",
" TotalBsmtSF | \n",
" Heating | \n",
" ... | \n",
" CentralAir | \n",
" Electrical | \n",
" 1stFlrSF | \n",
" 2ndFlrSF | \n",
" LowQualFinSF | \n",
" GrLivArea | \n",
" BsmtFullBath | \n",
" BsmtHalfBath | \n",
" FullBath | \n",
" HalfBath | \n",
" BedroomAbvGr | \n",
" KitchenAbvGr | \n",
" KitchenQual | \n",
" TotRmsAbvGrd | \n",
" Functional | \n",
" Fireplaces | \n",
" FireplaceQu | \n",
" GarageType | \n",
" GarageYrBlt | \n",
" GarageFinish | \n",
" GarageCars | \n",
" GarageArea | \n",
" GarageQual | \n",
" GarageCond | \n",
" PavedDrive | \n",
" WoodDeckSF | \n",
" OpenPorchSF | \n",
" EnclosedPorch | \n",
" 3SsnPorch | \n",
" ScreenPorch | \n",
" PoolArea | \n",
" PoolQC | \n",
" Fence | \n",
" MiscFeature | \n",
" MiscVal | \n",
" MoSold | \n",
" YrSold | \n",
" SaleType | \n",
" SaleCondition | \n",
" SalePrice | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 60 | \n",
" RL | \n",
" 65.0 | \n",
" 8450 | \n",
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" Norm | \n",
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" 7 | \n",
" 5 | \n",
" 2003 | \n",
" 2003 | \n",
" Gable | \n",
" CompShg | \n",
" VinylSd | \n",
" VinylSd | \n",
" BrkFace | \n",
" 196.0 | \n",
" Gd | \n",
" TA | \n",
" PConc | \n",
" Gd | \n",
" TA | \n",
" No | \n",
" GLQ | \n",
" 706 | \n",
" Unf | \n",
" 0 | \n",
" 150 | \n",
" 856 | \n",
" GasA | \n",
" ... | \n",
" Y | \n",
" SBrkr | \n",
" 856 | \n",
" 854 | \n",
" 0 | \n",
" 1710 | \n",
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" 8 | \n",
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" NaN | \n",
" Attchd | \n",
" 2003.0 | \n",
" RFn | \n",
" 2 | \n",
" 548 | \n",
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" TA | \n",
" Y | \n",
" 0 | \n",
" 61 | \n",
" 0 | \n",
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" 0 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 0 | \n",
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" WD | \n",
" Normal | \n",
" 208500 | \n",
"
\n",
" \n",
" 1 | \n",
" 2 | \n",
" 20 | \n",
" RL | \n",
" 80.0 | \n",
" 9600 | \n",
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" NaN | \n",
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" Lvl | \n",
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" FR2 | \n",
" Gtl | \n",
" Veenker | \n",
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" Norm | \n",
" 1Fam | \n",
" 1Story | \n",
" 6 | \n",
" 8 | \n",
" 1976 | \n",
" 1976 | \n",
" Gable | \n",
" CompShg | \n",
" MetalSd | \n",
" MetalSd | \n",
" None | \n",
" 0.0 | \n",
" TA | \n",
" TA | \n",
" CBlock | \n",
" Gd | \n",
" TA | \n",
" Gd | \n",
" ALQ | \n",
" 978 | \n",
" Unf | \n",
" 0 | \n",
" 284 | \n",
" 1262 | \n",
" GasA | \n",
" ... | \n",
" Y | \n",
" SBrkr | \n",
" 1262 | \n",
" 0 | \n",
" 0 | \n",
" 1262 | \n",
" 0 | \n",
" 1 | \n",
" 2 | \n",
" 0 | \n",
" 3 | \n",
" 1 | \n",
" TA | \n",
" 6 | \n",
" Typ | \n",
" 1 | \n",
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" 1976.0 | \n",
" RFn | \n",
" 2 | \n",
" 460 | \n",
" TA | \n",
" TA | \n",
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" 298 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 0 | \n",
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" WD | \n",
" Normal | \n",
" 181500 | \n",
"
\n",
" \n",
" 2 | \n",
" 3 | \n",
" 60 | \n",
" RL | \n",
" 68.0 | \n",
" 11250 | \n",
" Pave | \n",
" NaN | \n",
" IR1 | \n",
" Lvl | \n",
" AllPub | \n",
" Inside | \n",
" Gtl | \n",
" CollgCr | \n",
" Norm | \n",
" Norm | \n",
" 1Fam | \n",
" 2Story | \n",
" 7 | \n",
" 5 | \n",
" 2001 | \n",
" 2002 | \n",
" Gable | \n",
" CompShg | \n",
" VinylSd | \n",
" VinylSd | \n",
" BrkFace | \n",
" 162.0 | \n",
" Gd | \n",
" TA | \n",
" PConc | \n",
" Gd | \n",
" TA | \n",
" Mn | \n",
" GLQ | \n",
" 486 | \n",
" Unf | \n",
" 0 | \n",
" 434 | \n",
" 920 | \n",
" GasA | \n",
" ... | \n",
" Y | \n",
" SBrkr | \n",
" 920 | \n",
" 866 | \n",
" 0 | \n",
" 1786 | \n",
" 1 | \n",
" 0 | \n",
" 2 | \n",
" 1 | \n",
" 3 | \n",
" 1 | \n",
" Gd | \n",
" 6 | \n",
" Typ | \n",
" 1 | \n",
" TA | \n",
" Attchd | \n",
" 2001.0 | \n",
" RFn | \n",
" 2 | \n",
" 608 | \n",
" TA | \n",
" TA | \n",
" Y | \n",
" 0 | \n",
" 42 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 0 | \n",
" 9 | \n",
" 2008 | \n",
" WD | \n",
" Normal | \n",
" 223500 | \n",
"
\n",
" \n",
" 3 | \n",
" 4 | \n",
" 70 | \n",
" RL | \n",
" 60.0 | \n",
" 9550 | \n",
" Pave | \n",
" NaN | \n",
" IR1 | \n",
" Lvl | \n",
" AllPub | \n",
" Corner | \n",
" Gtl | \n",
" Crawfor | \n",
" Norm | \n",
" Norm | \n",
" 1Fam | \n",
" 2Story | \n",
" 7 | \n",
" 5 | \n",
" 1915 | \n",
" 1970 | \n",
" Gable | \n",
" CompShg | \n",
" Wd Sdng | \n",
" Wd Shng | \n",
" None | \n",
" 0.0 | \n",
" TA | \n",
" TA | \n",
" BrkTil | \n",
" TA | \n",
" Gd | \n",
" No | \n",
" ALQ | \n",
" 216 | \n",
" Unf | \n",
" 0 | \n",
" 540 | \n",
" 756 | \n",
" GasA | \n",
" ... | \n",
" Y | \n",
" SBrkr | \n",
" 961 | \n",
" 756 | \n",
" 0 | \n",
" 1717 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
" 3 | \n",
" 1 | \n",
" Gd | \n",
" 7 | \n",
" Typ | \n",
" 1 | \n",
" Gd | \n",
" Detchd | \n",
" 1998.0 | \n",
" Unf | \n",
" 3 | \n",
" 642 | \n",
" TA | \n",
" TA | \n",
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" 0 | \n",
" 35 | \n",
" 272 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 0 | \n",
" 2 | \n",
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" WD | \n",
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" 140000 | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 60 | \n",
" RL | \n",
" 84.0 | \n",
" 14260 | \n",
" Pave | \n",
" NaN | \n",
" IR1 | \n",
" Lvl | \n",
" AllPub | \n",
" FR2 | \n",
" Gtl | \n",
" NoRidge | \n",
" Norm | \n",
" Norm | \n",
" 1Fam | \n",
" 2Story | \n",
" 8 | \n",
" 5 | \n",
" 2000 | \n",
" 2000 | \n",
" Gable | \n",
" CompShg | \n",
" VinylSd | \n",
" VinylSd | \n",
" BrkFace | \n",
" 350.0 | \n",
" Gd | \n",
" TA | \n",
" PConc | \n",
" Gd | \n",
" TA | \n",
" Av | \n",
" GLQ | \n",
" 655 | \n",
" Unf | \n",
" 0 | \n",
" 490 | \n",
" 1145 | \n",
" GasA | \n",
" ... | \n",
" Y | \n",
" SBrkr | \n",
" 1145 | \n",
" 1053 | \n",
" 0 | \n",
" 2198 | \n",
" 1 | \n",
" 0 | \n",
" 2 | \n",
" 1 | \n",
" 4 | \n",
" 1 | \n",
" Gd | \n",
" 9 | \n",
" Typ | \n",
" 1 | \n",
" TA | \n",
" Attchd | \n",
" 2000.0 | \n",
" RFn | \n",
" 3 | \n",
" 836 | \n",
" TA | \n",
" TA | \n",
" Y | \n",
" 192 | \n",
" 84 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 0 | \n",
" 12 | \n",
" 2008 | \n",
" WD | \n",
" Normal | \n",
" 250000 | \n",
"
\n",
" \n",
"
\n",
"
5 rows × 81 columns
\n",
"
"
],
"text/plain": [
" Id MSSubClass MSZoning ... SaleType SaleCondition SalePrice\n",
"0 1 60 RL ... WD Normal 208500\n",
"1 2 20 RL ... WD Normal 181500\n",
"2 3 60 RL ... WD Normal 223500\n",
"3 4 70 RL ... WD Abnorml 140000\n",
"4 5 60 RL ... WD Normal 250000\n",
"\n",
"[5 rows x 81 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "hRT-l3CUYvgW",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 973,
"referenced_widgets": [
"84cd4922236448a3a306a3f6e7e43c88",
"47375cce9026442183ab428080b82e11",
"fefbae2c76574bf7b31541e5f1e022ef",
"562caa85cf7d4a2fbf4decac3fc6983b",
"30410561071f463ab76e219406f2fa9c",
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},
"outputId": "6834803b-b97a-478b-e92b-e262e6bdf6a6"
},
"source": [
"# Ignoring features with high null values \n",
"\n",
"demo = setup(data = data, target = 'SalePrice', \n",
" ignore_features = ['Alley','PoolQC','MiscFeature','Fence','FireplaceQu','Utilities'],normalize = True,\n",
" transformation= True, transformation_method = 'yeo-johnson', \n",
" transform_target = True, remove_outliers= True,\n",
" remove_multicollinearity = True,\n",
" ignore_low_variance = True, combine_rare_levels = True) "
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" \n",
"Setup Succesfully Completed.\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/html": [
" | Description | Value |
\n",
" \n",
" 0 | \n",
" session_id | \n",
" 2553 | \n",
"
\n",
" \n",
" 1 | \n",
" Transform Target | \n",
" True | \n",
"
\n",
" \n",
" 2 | \n",
" Transform Target Method | \n",
" box-cox | \n",
"
\n",
" \n",
" 3 | \n",
" Original Data | \n",
" (1460, 81) | \n",
"
\n",
" \n",
" 4 | \n",
" Missing Values | \n",
" True | \n",
"
\n",
" \n",
" 5 | \n",
" Numeric Features | \n",
" 21 | \n",
"
\n",
" \n",
" 6 | \n",
" Categorical Features | \n",
" 59 | \n",
"
\n",
" \n",
" 7 | \n",
" Ordinal Features | \n",
" False | \n",
"
\n",
" \n",
" 8 | \n",
" High Cardinality Features | \n",
" False | \n",
"
\n",
" \n",
" 9 | \n",
" High Cardinality Method | \n",
" None | \n",
"
\n",
" \n",
" 10 | \n",
" Sampled Data | \n",
" (1387, 81) | \n",
"
\n",
" \n",
" 11 | \n",
" Transformed Train Set | \n",
" (970, 244) | \n",
"
\n",
" \n",
" 12 | \n",
" Transformed Test Set | \n",
" (417, 244) | \n",
"
\n",
" \n",
" 13 | \n",
" Numeric Imputer | \n",
" mean | \n",
"
\n",
" \n",
" 14 | \n",
" Categorical Imputer | \n",
" constant | \n",
"
\n",
" \n",
" 15 | \n",
" Normalize | \n",
" True | \n",
"
\n",
" \n",
" 16 | \n",
" Normalize Method | \n",
" zscore | \n",
"
\n",
" \n",
" 17 | \n",
" Transformation | \n",
" True | \n",
"
\n",
" \n",
" 18 | \n",
" Transformation Method | \n",
" yeo-johnson | \n",
"
\n",
" \n",
" 19 | \n",
" PCA | \n",
" False | \n",
"
\n",
" \n",
" 20 | \n",
" PCA Method | \n",
" None | \n",
"
\n",
" \n",
" 21 | \n",
" PCA Components | \n",
" None | \n",
"
\n",
" \n",
" 22 | \n",
" Ignore Low Variance | \n",
" True | \n",
"
\n",
" \n",
" 23 | \n",
" Combine Rare Levels | \n",
" True | \n",
"
\n",
" \n",
" 24 | \n",
" Rare Level Threshold | \n",
" 0.100000 | \n",
"
\n",
" \n",
" 25 | \n",
" Numeric Binning | \n",
" False | \n",
"
\n",
" \n",
" 26 | \n",
" Remove Outliers | \n",
" True | \n",
"
\n",
" \n",
" 27 | \n",
" Outliers Threshold | \n",
" 0.050000 | \n",
"
\n",
" \n",
" 28 | \n",
" Remove Multicollinearity | \n",
" True | \n",
"
\n",
" \n",
" 29 | \n",
" Multicollinearity Threshold | \n",
" 0.900000 | \n",
"
\n",
" \n",
" 30 | \n",
" Clustering | \n",
" False | \n",
"
\n",
" \n",
" 31 | \n",
" Clustering Iteration | \n",
" None | \n",
"
\n",
" \n",
" 32 | \n",
" Polynomial Features | \n",
" False | \n",
"
\n",
" \n",
" 33 | \n",
" Polynomial Degree | \n",
" None | \n",
"
\n",
" \n",
" 34 | \n",
" Trignometry Features | \n",
" False | \n",
"
\n",
" \n",
" 35 | \n",
" Polynomial Threshold | \n",
" None | \n",
"
\n",
" \n",
" 36 | \n",
" Group Features | \n",
" False | \n",
"
\n",
" \n",
" 37 | \n",
" Feature Selection | \n",
" False | \n",
"
\n",
" \n",
" 38 | \n",
" Features Selection Threshold | \n",
" None | \n",
"
\n",
" \n",
" 39 | \n",
" Feature Interaction | \n",
" False | \n",
"
\n",
" \n",
" 40 | \n",
" Feature Ratio | \n",
" False | \n",
"
\n",
" \n",
" 41 | \n",
" Interaction Threshold | \n",
" None | \n",
"
\n",
"
"
],
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "v8tsvaw4aHHp",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
"cd5ab1a14de5402985b82653a139d818",
"6e28825ce0c1497287c54a26ed64fb9b",
"c45d6e58845e4a7e860c7fde14ed94d2"
]
},
"outputId": "1708f0d0-a5ff-4841-a3f5-634598ee507a"
},
"source": [
"# Blacklist Theil–Sen Regressor \n",
"# Auto sort on R2 \n",
"compare_models(blacklist = ['tr'])"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
" | Model | MAE | MSE | RMSE | R2 | RMSLE | MAPE | TT (Sec) |
\n",
" \n",
" 0 | \n",
" CatBoost Regressor | \n",
" 15313.6607 | \n",
" 776434230.5449 | \n",
" 25574.6520 | \n",
" 0.8966 | \n",
" 0.1216 | \n",
" 0.0854 | \n",
" 6.0545 | \n",
"
\n",
" \n",
" 1 | \n",
" Huber Regressor | \n",
" 14604.9714 | \n",
" 912236241.9322 | \n",
" 25213.0988 | \n",
" 0.8939 | \n",
" 0.1190 | \n",
" 0.0833 | \n",
" 0.1802 | \n",
"
\n",
" \n",
" 2 | \n",
" Support Vector Machine | \n",
" 15387.0710 | \n",
" 795925878.7054 | \n",
" 26206.2707 | \n",
" 0.8893 | \n",
" 0.1252 | \n",
" 0.0865 | \n",
" 0.2447 | \n",
"
\n",
" \n",
" 3 | \n",
" Bayesian Ridge | \n",
" 15381.1023 | \n",
" 920602470.6363 | \n",
" 26383.7903 | \n",
" 0.8880 | \n",
" 0.1212 | \n",
" 0.0855 | \n",
" 0.0722 | \n",
"
\n",
" \n",
" 4 | \n",
" Light Gradient Boosting Machine | \n",
" 16799.0399 | \n",
" 837267455.9643 | \n",
" 27326.6238 | \n",
" 0.8817 | \n",
" 0.1303 | \n",
" 0.0925 | \n",
" 0.2839 | \n",
"
\n",
" \n",
" 5 | \n",
" Gradient Boosting Regressor | \n",
" 16988.6879 | \n",
" 900162869.8805 | \n",
" 27414.7745 | \n",
" 0.8814 | \n",
" 0.1312 | \n",
" 0.0943 | \n",
" 0.7129 | \n",
"
\n",
" \n",
" 6 | \n",
" Extreme Gradient Boosting | \n",
" 17203.8522 | \n",
" 890529209.3684 | \n",
" 27723.6269 | \n",
" 0.8793 | \n",
" 0.1338 | \n",
" 0.0964 | \n",
" 0.4334 | \n",
"
\n",
" \n",
" 7 | \n",
" Ridge Regression | \n",
" 16198.3462 | \n",
" 996008412.4994 | \n",
" 27658.9915 | \n",
" 0.8776 | \n",
" 0.1255 | \n",
" 0.0893 | \n",
" 0.0130 | \n",
"
\n",
" \n",
" 8 | \n",
" Random Forest | \n",
" 17884.1543 | \n",
" 918488672.4057 | \n",
" 28876.3710 | \n",
" 0.8683 | \n",
" 0.1407 | \n",
" 0.0996 | \n",
" 1.5661 | \n",
"
\n",
" \n",
" 9 | \n",
" Orthogonal Matching Pursuit | \n",
" 17525.4920 | \n",
" 1180290533.4058 | \n",
" 30383.1262 | \n",
" 0.8544 | \n",
" 0.1384 | \n",
" 0.0986 | \n",
" 0.0142 | \n",
"
\n",
" \n",
" 10 | \n",
" K Neighbors Regressor | \n",
" 20935.8666 | \n",
" 1263777102.8334 | \n",
" 33958.8645 | \n",
" 0.8201 | \n",
" 0.1578 | \n",
" 0.1129 | \n",
" 0.0197 | \n",
"
\n",
" \n",
" 11 | \n",
" Linear Regression | \n",
" 18579.5821 | \n",
" 1412999991.7642 | \n",
" 34303.1362 | \n",
" 0.8149 | \n",
" 0.8875 | \n",
" 0.1016 | \n",
" 0.0375 | \n",
"
\n",
" \n",
" 12 | \n",
" Extra Trees Regressor | \n",
" 21077.7765 | \n",
" 1450475526.5097 | \n",
" 37113.8801 | \n",
" 0.7864 | \n",
" 0.1648 | \n",
" 0.1114 | \n",
" 1.5526 | \n",
"
\n",
" \n",
" 13 | \n",
" Passive Aggressive Regressor | \n",
" 23476.6379 | \n",
" 1455925010.0713 | \n",
" 34983.9284 | \n",
" 0.7832 | \n",
" 0.1663 | \n",
" 0.1258 | \n",
" 0.0310 | \n",
"
\n",
" \n",
" 14 | \n",
" AdaBoost Regressor | \n",
" 24871.3695 | \n",
" 1596338786.3069 | \n",
" 38377.7193 | \n",
" 0.7702 | \n",
" 0.1783 | \n",
" 0.1330 | \n",
" 0.4744 | \n",
"
\n",
" \n",
" 15 | \n",
" Random Sample Consensus | \n",
" 20436.4470 | \n",
" 2024540158.8414 | \n",
" 42271.7509 | \n",
" 0.7199 | \n",
" 1.8876 | \n",
" 0.1166 | \n",
" 2.4091 | \n",
"
\n",
" \n",
" 16 | \n",
" Decision Tree | \n",
" 28313.9691 | \n",
" 2362204385.5155 | \n",
" 46922.6999 | \n",
" 0.6505 | \n",
" 0.2193 | \n",
" 0.1556 | \n",
" 0.0383 | \n",
"
\n",
" \n",
" 17 | \n",
" Elastic Net | \n",
" 48540.7327 | \n",
" 5644074263.3008 | \n",
" 73662.8744 | \n",
" 0.1642 | \n",
" 0.3367 | \n",
" 0.2630 | \n",
" 0.0100 | \n",
"
\n",
" \n",
" 18 | \n",
" Lasso Regression | \n",
" 55943.9350 | \n",
" 6918562280.5182 | \n",
" 81883.4356 | \n",
" -0.0385 | \n",
" 0.3877 | \n",
" 0.3102 | \n",
" 0.0097 | \n",
"
\n",
" \n",
" 19 | \n",
" Lasso Least Angle Regression | \n",
" 55943.9350 | \n",
" 6918562280.5182 | \n",
" 81883.4356 | \n",
" -0.0385 | \n",
" 0.3877 | \n",
" 0.3102 | \n",
" 0.0123 | \n",
"
\n",
" \n",
" 20 | \n",
" Least Angle Regression | \n",
" 5993932134656973907951616.0000 | \n",
" 34849194485222397251019231724335227216317654899884032.0000 | \n",
" 59033380261709037822803968.0000 | \n",
" -5421370651881711978246612473249811579011072.0000 | \n",
" 9.7590 | \n",
" 31547047712188772352.0000 | \n",
" 0.1863 | \n",
"
\n",
"
"
],
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "wXxDMgpWe_nj",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297,
"referenced_widgets": [
"3c29f19233864b53ba8da01af51d572a",
"c59e1582725c43f282f196e76d7f8883",
"629dd21d247a4a63a3705add599b59dd",
"5ea1974eab714e209d781a04564f38aa",
"51b30e1917584733bcc1ad280f657a6f",
"e3c23703d9304ea09d7233dadc0b067f",
"3d8c5beac0da40168f86729f70acbb79",
"08c8a60ba9e94046bd2d98afe7cc741a",
"367ad9468f49427ca8475e5301a95ddb"
]
},
"outputId": "1e60ba0d-cf32-4c8e-cb9b-a084e6a0106d"
},
"source": [
"# Creating models for the best estimators \n",
"huber = create_model('huber')\n",
"bayesian_ridge = create_model('br')\n",
"cat_boost = create_model('catboost')"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
" | MAE | MSE | RMSE | R2 | RMSLE | MAPE |
\n",
" \n",
" 0 | \n",
" 14464.0462 | \n",
" 796631140.1868 | \n",
" 28224.6548 | \n",
" 0.8678 | \n",
" 0.1055 | \n",
" 0.0744 | \n",
"
\n",
" \n",
" 1 | \n",
" 12317.9481 | \n",
" 486929512.7377 | \n",
" 22066.4794 | \n",
" 0.8870 | \n",
" 0.1081 | \n",
" 0.0685 | \n",
"
\n",
" \n",
" 2 | \n",
" 15872.2935 | \n",
" 582156173.3834 | \n",
" 24127.9127 | \n",
" 0.9094 | \n",
" 0.1512 | \n",
" 0.1058 | \n",
"
\n",
" \n",
" 3 | \n",
" 16292.3554 | \n",
" 623678424.8092 | \n",
" 24973.5545 | \n",
" 0.9091 | \n",
" 0.1106 | \n",
" 0.0862 | \n",
"
\n",
" \n",
" 4 | \n",
" 25111.9159 | \n",
" 3283563129.2471 | \n",
" 57302.3833 | \n",
" 0.7460 | \n",
" 0.1700 | \n",
" 0.1107 | \n",
"
\n",
" \n",
" 5 | \n",
" 12310.8000 | \n",
" 301712699.9034 | \n",
" 17369.8791 | \n",
" 0.9566 | \n",
" 0.0936 | \n",
" 0.0705 | \n",
"
\n",
" \n",
" 6 | \n",
" 14194.3649 | \n",
" 427569376.3631 | \n",
" 20677.7508 | \n",
" 0.9369 | \n",
" 0.1294 | \n",
" 0.0855 | \n",
"
\n",
" \n",
" 7 | \n",
" 15042.6148 | \n",
" 463198310.0888 | \n",
" 21522.0424 | \n",
" 0.8863 | \n",
" 0.1489 | \n",
" 0.1021 | \n",
"
\n",
" \n",
" 8 | \n",
" 15624.3454 | \n",
" 522717385.2126 | \n",
" 22863.0135 | \n",
" 0.9212 | \n",
" 0.1124 | \n",
" 0.0843 | \n",
"
\n",
" \n",
" 9 | \n",
" 11905.9229 | \n",
" 276186153.5170 | \n",
" 16618.8493 | \n",
" 0.9455 | \n",
" 0.0864 | \n",
" 0.0654 | \n",
"
\n",
" \n",
" Mean | \n",
" 15313.6607 | \n",
" 776434230.5449 | \n",
" 25574.6520 | \n",
" 0.8966 | \n",
" 0.1216 | \n",
" 0.0854 | \n",
"
\n",
" \n",
" SD | \n",
" 3591.9983 | \n",
" 847985780.9750 | \n",
" 11062.1610 | \n",
" 0.0568 | \n",
" 0.0259 | \n",
" 0.0154 | \n",
"
\n",
"
"
],
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "RyfTEP0RhzAy",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297,
"referenced_widgets": [
"838a3f746e6a4b6b9f3d05d6c3d506cb",
"3a2d5a31c1d1444ab603e67934aa6acc",
"11fdc00d65ad4d82950b754c7f5ef6b0",
"3e621330c9144f568bdc0eef124afb1a",
"ce93bca57c684aa3b46265d0c37eb746",
"c485ec5deb47455fad921d447491bb7f",
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]
},
"outputId": "65c8f299-4e28-4131-8d96-6523ab5edfca"
},
"source": [
"# Tuning the created models \n",
"huber = tune_model(huber)\n",
"bayesian_ridge = tune_model(bayesian_ridge)\n",
"cat_boost = tune_model(cat_boost)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
" | MAE | MSE | RMSE | R2 | RMSLE | MAPE |
\n",
" \n",
" 0 | \n",
" 15222.1797 | \n",
" 670566500.0937 | \n",
" 25895.2988 | \n",
" 0.8887 | \n",
" 0.1090 | \n",
" 0.0828 | \n",
"
\n",
" \n",
" 1 | \n",
" 12700.3444 | \n",
" 471383401.7861 | \n",
" 21711.3657 | \n",
" 0.8906 | \n",
" 0.1074 | \n",
" 0.0704 | \n",
"
\n",
" \n",
" 2 | \n",
" 16694.3988 | \n",
" 633569183.1174 | \n",
" 25170.8002 | \n",
" 0.9014 | \n",
" 0.1566 | \n",
" 0.1098 | \n",
"
\n",
" \n",
" 3 | \n",
" 17282.1157 | \n",
" 742530620.7051 | \n",
" 27249.4151 | \n",
" 0.8918 | \n",
" 0.1192 | \n",
" 0.0909 | \n",
"
\n",
" \n",
" 4 | \n",
" 27664.4349 | \n",
" 3328893846.1072 | \n",
" 57696.5670 | \n",
" 0.7424 | \n",
" 0.1762 | \n",
" 0.1211 | \n",
"
\n",
" \n",
" 5 | \n",
" 13631.0586 | \n",
" 342263029.3862 | \n",
" 18500.3521 | \n",
" 0.9508 | \n",
" 0.1028 | \n",
" 0.0786 | \n",
"
\n",
" \n",
" 6 | \n",
" 14448.9475 | \n",
" 421609729.3052 | \n",
" 20533.1373 | \n",
" 0.9378 | \n",
" 0.1256 | \n",
" 0.0870 | \n",
"
\n",
" \n",
" 7 | \n",
" 16189.5874 | \n",
" 533125793.0337 | \n",
" 23089.5170 | \n",
" 0.8691 | \n",
" 0.1575 | \n",
" 0.1096 | \n",
"
\n",
" \n",
" 8 | \n",
" 16572.7477 | \n",
" 600491213.8336 | \n",
" 24504.9222 | \n",
" 0.9094 | \n",
" 0.1163 | \n",
" 0.0884 | \n",
"
\n",
" \n",
" 9 | \n",
" 12919.5620 | \n",
" 300757106.7155 | \n",
" 17342.3501 | \n",
" 0.9406 | \n",
" 0.0914 | \n",
" 0.0709 | \n",
"
\n",
" \n",
" Mean | \n",
" 16332.5377 | \n",
" 804519042.4084 | \n",
" 26169.3726 | \n",
" 0.8923 | \n",
" 0.1262 | \n",
" 0.0909 | \n",
"
\n",
" \n",
" SD | \n",
" 4079.7390 | \n",
" 852280183.3400 | \n",
" 10939.9718 | \n",
" 0.0559 | \n",
" 0.0264 | \n",
" 0.0164 | \n",
"
\n",
"
"
],
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "rjXOLZR6lTRf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297,
"referenced_widgets": [
"47c66341f17d40de815a82eedaf43dba",
"c77287c6a0dd4892b02f8e94f83360d6",
"2656c119068c4faa86003a43ddb52453"
]
},
"outputId": "da06d194-0efd-40e3-9b50-f35120d5e76c"
},
"source": [
"# Blending models\n",
"blender = blend_models(estimator_list = [huber, bayesian_ridge, cat_boost])"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
" | MAE | MSE | RMSE | R2 | RMSLE | MAPE |
\n",
" \n",
" 0 | \n",
" 13233.5963 | \n",
" 463761320.6194 | \n",
" 21535.1183 | \n",
" 0.9230 | \n",
" 0.0971 | \n",
" 0.0713 | \n",
"
\n",
" \n",
" 1 | \n",
" 11504.8842 | \n",
" 413827989.5399 | \n",
" 20342.7626 | \n",
" 0.9039 | \n",
" 0.1018 | \n",
" 0.0655 | \n",
"
\n",
" \n",
" 2 | \n",
" 14194.1861 | \n",
" 464560920.4938 | \n",
" 21553.6753 | \n",
" 0.9277 | \n",
" 0.1431 | \n",
" 0.0960 | \n",
"
\n",
" \n",
" 3 | \n",
" 14860.3248 | \n",
" 579523794.9158 | \n",
" 24073.3005 | \n",
" 0.9156 | \n",
" 0.1046 | \n",
" 0.0793 | \n",
"
\n",
" \n",
" 4 | \n",
" 25506.1659 | \n",
" 4270880912.6006 | \n",
" 65351.9771 | \n",
" 0.6696 | \n",
" 0.1825 | \n",
" 0.1149 | \n",
"
\n",
" \n",
" 5 | \n",
" 12604.3339 | \n",
" 295248634.9563 | \n",
" 17182.8006 | \n",
" 0.9576 | \n",
" 0.0916 | \n",
" 0.0708 | \n",
"
\n",
" \n",
" 6 | \n",
" 12364.6512 | \n",
" 352687304.7260 | \n",
" 18779.9708 | \n",
" 0.9479 | \n",
" 0.1155 | \n",
" 0.0750 | \n",
"
\n",
" \n",
" 7 | \n",
" 14421.4822 | \n",
" 438085842.0711 | \n",
" 20930.5003 | \n",
" 0.8924 | \n",
" 0.1395 | \n",
" 0.0943 | \n",
"
\n",
" \n",
" 8 | \n",
" 13692.8294 | \n",
" 351155335.6424 | \n",
" 18739.1391 | \n",
" 0.9470 | \n",
" 0.1016 | \n",
" 0.0777 | \n",
"
\n",
" \n",
" 9 | \n",
" 10979.4809 | \n",
" 232445546.8403 | \n",
" 15246.1650 | \n",
" 0.9541 | \n",
" 0.0821 | \n",
" 0.0619 | \n",
"
\n",
" \n",
" Mean | \n",
" 14336.1935 | \n",
" 786217760.2406 | \n",
" 24373.5410 | \n",
" 0.9039 | \n",
" 0.1160 | \n",
" 0.0807 | \n",
"
\n",
" \n",
" SD | \n",
" 3909.8622 | \n",
" 1165240457.9300 | \n",
" 13861.7553 | \n",
" 0.0808 | \n",
" 0.0289 | \n",
" 0.0155 | \n",
"
\n",
"
"
],
"text/plain": [
""
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "dT_dnYL1na25",
"colab": {}
},
"source": [
"# Finaliszing model for predictions \n",
"model = finalize_model(blender)\n",
"predictions = predict_model(model, data = test_data)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "_3lQxyHcoCzH",
"colab": {}
},
"source": [
"# Generating CSV for Kaggle Submissions \n",
"sub = pd.DataFrame({\n",
" \"Id\": predictions['Id'],\n",
" \"SalePrice\": predictions['Label']\n",
" })\n",
"\n",
"sub.to_csv('gdrive/My Drive/Colab Notebooks/HousePrice/submission.csv', index=False)"
],
"execution_count": null,
"outputs": []
}
]
}