mirror of
https://github.com/ashishpatel26/Amazing-Feature-Engineering.git
synced 2022-05-07 18:26:02 +03:00
327 lines
8.7 KiB
Plaintext
327 lines
8.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"# import seaborn as sns\n",
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"# import matplotlib.pyplot as plt\n",
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"import os\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"# plt.style.use('seaborn-colorblind')\n",
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"# %matplotlib inline\n",
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"#from feature_cleaning import rare_values as ra"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load Dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"use_cols = [\n",
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" 'Pclass', 'Sex', 'Age', 'Fare', 'SibSp',\n",
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" 'Survived'\n",
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"]\n",
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"\n",
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"data = pd.read_csv('./data/titanic.csv', usecols=use_cols)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Survived</th>\n",
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" <th>Pclass</th>\n",
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" <th>Sex</th>\n",
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" <th>Age</th>\n",
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" <th>SibSp</th>\n",
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" <th>Fare</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0</td>\n",
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" <td>3</td>\n",
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" <td>male</td>\n",
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" <td>22.0</td>\n",
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" <td>1</td>\n",
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" <td>7.2500</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>female</td>\n",
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" <td>38.0</td>\n",
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" <td>1</td>\n",
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" <td>71.2833</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1</td>\n",
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" <td>3</td>\n",
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" <td>female</td>\n",
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" <td>26.0</td>\n",
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" <td>0</td>\n",
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" <td>7.9250</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Survived Pclass Sex Age SibSp Fare\n",
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"0 0 3 male 22.0 1 7.2500\n",
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"1 1 1 female 38.0 1 71.2833\n",
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"2 1 3 female 26.0 0 7.9250"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data.head(3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"((623, 6), (268, 6))"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Note that we include target variable in the X_train \n",
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"# because we need it to supervise our discretization\n",
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"# this is not the standard way of using train-test-split\n",
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"X_train, X_test, y_train, y_test = train_test_split(data, data.Survived, test_size=0.3,\n",
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" random_state=0)\n",
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"X_train.shape, X_test.shape"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Normalization - Standardization (Z-score scaling)\n",
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"\n",
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"removes the mean and scales the data to unit variance.<br />z = (X - X.mean) / std"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Survived Pclass Sex Age SibSp Fare Fare_zscore\n",
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"857 1 1 male 51.0 0 26.5500 -0.122530\n",
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"52 1 1 female 49.0 1 76.7292 0.918124\n",
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"386 0 3 male 1.0 5 46.9000 0.299503\n",
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"124 0 1 male 54.0 0 77.2875 0.929702\n",
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"578 0 3 female NaN 1 14.4583 -0.373297\n",
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"549 1 2 male 8.0 1 36.7500 0.089005\n"
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]
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}
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],
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"source": [
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"# add the new created feature\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"ss = StandardScaler().fit(X_train[['Fare']])\n",
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"X_train_copy = X_train.copy(deep=True)\n",
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"X_train_copy['Fare_zscore'] = ss.transform(X_train_copy[['Fare']])\n",
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"print(X_train_copy.head(6))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"5.916437306188636e-17\n",
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"1.0008035356861\n"
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]
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}
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],
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"source": [
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"# check if it is with mean=0 std=1\n",
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"print(X_train_copy['Fare_zscore'].mean())\n",
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"print(X_train_copy['Fare_zscore'].std())\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Min-Max scaling\n",
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"transforms features by scaling each feature to a given range. Default to [0,1].<br />X_scaled = (X - X.min / (X.max - X.min)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Survived Pclass Sex Age SibSp Fare Fare_minmax\n",
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"857 1 1 male 51.0 0 26.5500 0.051822\n",
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"52 1 1 female 49.0 1 76.7292 0.149765\n",
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"386 0 3 male 1.0 5 46.9000 0.091543\n",
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"124 0 1 male 54.0 0 77.2875 0.150855\n",
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"578 0 3 female NaN 1 14.4583 0.028221\n",
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"549 1 2 male 8.0 1 36.7500 0.071731\n"
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]
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}
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],
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"source": [
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"# add the new created feature\n",
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"from sklearn.preprocessing import MinMaxScaler\n",
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"mms = MinMaxScaler().fit(X_train[['Fare']])\n",
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"X_train_copy = X_train.copy(deep=True)\n",
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"X_train_copy['Fare_minmax'] = mms.transform(X_train_copy[['Fare']])\n",
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"print(X_train_copy.head(6))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1.0\n",
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"0.0\n"
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]
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}
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],
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"source": [
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"# check the range of Fare_minmax\n",
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"print(X_train_copy['Fare_minmax'].max())\n",
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"print(X_train_copy['Fare_minmax'].min())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"## Robust scaling\n",
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"removes the median and scales the data according to the quantile range (defaults to IQR)<br />X_scaled = (X - X.median) / IQR"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Survived Pclass Sex Age SibSp Fare Fare_robust\n",
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"857 1 1 male 51.0 0 26.5500 0.492275\n",
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"52 1 1 female 49.0 1 76.7292 2.630973\n",
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"386 0 3 male 1.0 5 46.9000 1.359616\n",
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"124 0 1 male 54.0 0 77.2875 2.654768\n",
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"578 0 3 female NaN 1 14.4583 -0.023088\n",
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"549 1 2 male 8.0 1 36.7500 0.927011\n"
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]
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}
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],
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"source": [
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"# add the new created feature\n",
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"from sklearn.preprocessing import RobustScaler\n",
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"rs = RobustScaler().fit(X_train[['Fare']])\n",
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"X_train_copy = X_train.copy(deep=True)\n",
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"X_train_copy['Fare_robust'] = rs.transform(X_train_copy[['Fare']])\n",
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"print(X_train_copy.head(6))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"file_extension": ".py",
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"nbconvert_exporter": "python",
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