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https://github.com/fenago/data-science.git
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fenago
646 lines
16 KiB
Plaintext
646 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "8UMFqsCD0xyF"
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},
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"outputs": [],
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"source": [
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"# Importing necessary packages\n",
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"import pandas as pd\n",
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"import numpy as np"
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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": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "HSXgY0ze09cY"
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},
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"outputs": [],
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"source": [
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"file_url = '../bank-full.csv'\n",
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"bankData = pd.read_csv(file_url, sep=\";\")"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 204
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},
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"colab_type": "code",
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"executionInfo": {
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"elapsed": 8374,
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"status": "ok",
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"timestamp": 1573003026166,
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"user": {
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"displayName": "Anthony So",
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"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCYY-iGjUIqBSnlLoszfZTN7rU7FRNg05Rdt9Ii3A=s64",
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"userId": "11809607246124237079"
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},
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"user_tz": -660
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},
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"id": "ecnqNxm0TZay",
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"outputId": "515916d0-69e2-4b67-8da1-b11ebc7bb5d0"
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},
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"outputs": [],
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"source": [
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"# Normalising data\n",
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"from sklearn import preprocessing\n",
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"x = bankData[['balance']].values.astype(float)\n",
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"# Creating the scaling function\n",
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"minmaxScaler = preprocessing.MinMaxScaler()\n",
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"# Transforming the balance data by normalising it with minmaxScalre\n",
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"bankData['balanceTran'] = minmaxScaler.fit_transform(x)\n",
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"# Printing the head of the data\n",
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"bankData.head()"
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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": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "oISJ1v9sTg_S"
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},
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"outputs": [],
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"source": [
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"# Adding a small numerical constant to eliminate 0 values\n",
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"\n",
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"bankData['balanceTran'] = bankData['balanceTran'] + 0.00001"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 224
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},
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"colab_type": "code",
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"executionInfo": {
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"elapsed": 1190,
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"status": "ok",
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"timestamp": 1573003030600,
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"user": {
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"displayName": "Anthony So",
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"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCYY-iGjUIqBSnlLoszfZTN7rU7FRNg05Rdt9Ii3A=s64",
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"userId": "11809607246124237079"
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},
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"user_tz": -660
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},
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"id": "GsDGKLQzTy9O",
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"outputId": "d89e4bc9-09e9-4f54-d071-b2edc3784914"
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},
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"outputs": [],
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"source": [
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"# Let us transform values for loan data\n",
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"bankData['loanTran'] = 1\n",
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"# Giving a weight of 5 if there is no loan\n",
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"bankData.loc[bankData['loan'] == 'no', 'loanTran'] = 5\n",
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"bankData.head()"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 224
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},
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"colab_type": "code",
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"executionInfo": {
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"elapsed": 1415,
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"status": "ok",
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"timestamp": 1573003040146,
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"user": {
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"displayName": "Anthony So",
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"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCYY-iGjUIqBSnlLoszfZTN7rU7FRNg05Rdt9Ii3A=s64",
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"userId": "11809607246124237079"
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},
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"user_tz": -660
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},
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"id": "tPrwaWORT2wt",
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"outputId": "3687fb10-b25c-4ad4-d818-8f0ba4b6965c"
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},
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"outputs": [],
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"source": [
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"# Let us transform values for Housing data\n",
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"bankData['houseTran'] = 5\n",
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"# Giving a weight of 1 if the customer has a house\n",
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"bankData.loc[bankData['housing'] == 'no', 'houseTran'] = 1\n",
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"\n",
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"bankData.head()"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 224
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},
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"colab_type": "code",
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"executionInfo": {
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"elapsed": 1023,
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"status": "ok",
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"timestamp": 1573003043755,
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"user": {
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"displayName": "Anthony So",
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"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCYY-iGjUIqBSnlLoszfZTN7rU7FRNg05Rdt9Ii3A=s64",
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"userId": "11809607246124237079"
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},
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"user_tz": -660
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},
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"id": "C3a2zadTUGLq",
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"outputId": "ce60c876-3080-43c3-b3fa-8e44d17a123f"
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},
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"outputs": [],
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"source": [
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"# Let us now create the new variable which is a product of all these\n",
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"bankData['assetIndex'] = bankData['balanceTran'] * bankData['loanTran'] * bankData['houseTran']\n",
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"bankData.head()"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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},
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"colab_type": "code",
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"executionInfo": {
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"elapsed": 1199,
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"status": "ok",
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"timestamp": 1573003063439,
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"user": {
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"displayName": "Anthony So",
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"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCYY-iGjUIqBSnlLoszfZTN7rU7FRNg05Rdt9Ii3A=s64",
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"userId": "11809607246124237079"
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},
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"user_tz": -660
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},
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"id": "I2eFF9GLUSn0",
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"outputId": "68c4ab47-ac24-4e42-9588-b6414d6826fd"
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},
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"outputs": [],
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"source": [
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"# Finding the quantile\n",
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"np.quantile(bankData['assetIndex'],[0.25,0.5,0.75])"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 224
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},
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"colab_type": "code",
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"executionInfo": {
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"elapsed": 1500,
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"status": "ok",
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"timestamp": 1573003081578,
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"user": {
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"displayName": "Anthony So",
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"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCYY-iGjUIqBSnlLoszfZTN7rU7FRNg05Rdt9Ii3A=s64",
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"userId": "11809607246124237079"
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},
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"user_tz": -660
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},
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"id": "dklJXEaNUbf7",
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"outputId": "646550ac-4c22-494c-a792-fa8e5c7d9b97"
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},
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"outputs": [],
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"source": [
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"# Creating quantiles from the assetindex data\n",
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"bankData['assetClass'] = 'Quant1'\n",
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"\n",
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"bankData.loc[(bankData['assetIndex'] > 0.38) & (bankData['assetIndex'] < 0.57), 'assetClass'] = 'Quant2'\n",
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"\n",
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"bankData.loc[(bankData['assetIndex'] > 0.57) & (bankData['assetIndex'] < 1.9), 'assetClass'] = 'Quant3'\n",
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"\n",
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"bankData.loc[bankData['assetIndex'] > 1.9, 'assetClass'] = 'Quant4'\n",
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"\n",
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"bankData.head()"
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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": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "JAt_inPjSo75"
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},
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"outputs": [],
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"source": [
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"# Calculating total of each asset class\n",
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"assetTot = bankData.groupby('assetClass')['y'].agg(assetTot='count').reset_index()\n",
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"# Calculating the category wise counts\n",
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"assetProp = bankData.groupby(['assetClass', 'y'])['y'].agg(assetCat='count').reset_index()"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 297
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},
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"colab_type": "code",
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"executionInfo": {
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"elapsed": 1143,
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"status": "ok",
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"timestamp": 1573003235697,
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"user": {
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"displayName": "Anthony So",
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"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCYY-iGjUIqBSnlLoszfZTN7rU7FRNg05Rdt9Ii3A=s64",
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"userId": "11809607246124237079"
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},
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"user_tz": -660
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},
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"id": "tTGT5nBGSzgP",
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"outputId": "5a262e17-17ba-420e-ec7d-e9b470723f7f"
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},
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"outputs": [],
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"source": [
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"# Merging both the data frames\n",
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"assetComb = pd.merge(assetProp, assetTot, on=['assetClass'])\n",
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"assetComb['catProp'] = (assetComb.assetCat / assetComb.assetTot)*100\n",
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"assetComb"
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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": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "iJG_qZRzlVc1"
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},
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"outputs": [],
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"source": [
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"# Categorical variables, removing loan and housing\n",
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"bankCat1 = pd.get_dummies(bankData[['job','marital','education','default','contact','month','poutcome']])"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 204
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},
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"colab_type": "code",
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"executionInfo": {
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"elapsed": 1007,
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"status": "ok",
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"timestamp": 1573003254182,
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"user": {
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"displayName": "Anthony So",
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"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCYY-iGjUIqBSnlLoszfZTN7rU7FRNg05Rdt9Ii3A=s64",
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"userId": "11809607246124237079"
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},
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"user_tz": -660
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},
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"id": "N6j-jur1ljLP",
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"outputId": "567676f2-e3e5-48cf-8308-6a7276c09051"
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},
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"outputs": [],
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"source": [
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"bankNum1 = bankData[['age','day','duration','campaign','pdays','previous','assetIndex']]\n",
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"bankNum1.head()"
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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": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "rhbHeJ3PlyiM"
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},
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"outputs": [],
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"source": [
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"# Normalise some of the numerical variables\n",
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"from sklearn import preprocessing"
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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": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "zG-wd9qgl4xW"
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},
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"outputs": [],
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"source": [
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"# Creating the scaling function\n",
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"minmaxScaler = preprocessing.MinMaxScaler()"
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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": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "3wDY6fPfmAki"
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},
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"outputs": [],
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"source": [
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"# Creating the transformation variables\n",
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"ageT1 = bankNum1[['age']].values.astype(float)\n",
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"dayT1 = bankNum1[['day']].values.astype(float)\n",
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"durT1 = bankNum1[['duration']].values.astype(float)"
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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": null,
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"metadata": {
|
|
"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 323
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},
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"colab_type": "code",
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"executionInfo": {
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|
"elapsed": 1121,
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"status": "ok",
|
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"timestamp": 1573003270320,
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"user": {
|
|
"displayName": "Anthony So",
|
|
"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCYY-iGjUIqBSnlLoszfZTN7rU7FRNg05Rdt9Ii3A=s64",
|
|
"userId": "11809607246124237079"
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|
},
|
|
"user_tz": -660
|
|
},
|
|
"id": "5MT1CP24mDBx",
|
|
"outputId": "628d9b83-87b0-476a-9331-6a09ed5eb071"
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|
},
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"outputs": [],
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|
"source": [
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"# Transforming the balance data by normalising it with minmaxScalre\n",
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"bankNum1['ageTran'] = minmaxScaler.fit_transform(ageT1)\n",
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"bankNum1['dayTran'] = minmaxScaler.fit_transform(dayT1)\n",
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"bankNum1['durTran'] = minmaxScaler.fit_transform(durT1)"
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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": null,
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|
"metadata": {
|
|
"colab": {
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|
"base_uri": "https://localhost:8080/",
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|
"height": 204
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|
},
|
|
"colab_type": "code",
|
|
"executionInfo": {
|
|
"elapsed": 1345,
|
|
"status": "ok",
|
|
"timestamp": 1573003276269,
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|
"user": {
|
|
"displayName": "Anthony So",
|
|
"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCYY-iGjUIqBSnlLoszfZTN7rU7FRNg05Rdt9Ii3A=s64",
|
|
"userId": "11809607246124237079"
|
|
},
|
|
"user_tz": -660
|
|
},
|
|
"id": "L2LfnZDWmNJ3",
|
|
"outputId": "6e72f0da-1dfc-4376-c9e5-c4a261e265f6"
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|
},
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|
"outputs": [],
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"source": [
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"# Let us create a new numerical variable by selecting the transformed variables\n",
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"bankNum2 = bankNum1[['ageTran','dayTran','durTran','campaign','pdays','previous','assetIndex']]\n",
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"\n",
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"# Printing the head of the data\n",
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"bankNum2.head()"
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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": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 275
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|
},
|
|
"colab_type": "code",
|
|
"executionInfo": {
|
|
"elapsed": 1430,
|
|
"status": "ok",
|
|
"timestamp": 1573003278578,
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|
"user": {
|
|
"displayName": "Anthony So",
|
|
"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCYY-iGjUIqBSnlLoszfZTN7rU7FRNg05Rdt9Ii3A=s64",
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|
"userId": "11809607246124237079"
|
|
},
|
|
"user_tz": -660
|
|
},
|
|
"id": "hD6suIpqmYTd",
|
|
"outputId": "58fc69cb-ff8b-483e-8286-5b3f863a7f2f"
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|
},
|
|
"outputs": [],
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"source": [
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"# Preparing the X variables\n",
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"X = pd.concat([bankCat1, bankNum2], axis=1)\n",
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"print(X.shape)\n",
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"# Preparing the Y variable\n",
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"Y = bankData['y']\n",
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"print(Y.shape)\n",
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"X.head()"
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]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {},
|
|
"colab_type": "code",
|
|
"id": "4c7Jyxymmiaj"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"\n",
|
|
"from sklearn.model_selection import train_test_split\n",
|
|
"# Splitting the data into train and test sets\n",
|
|
"X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=123)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 156
|
|
},
|
|
"colab_type": "code",
|
|
"executionInfo": {
|
|
"elapsed": 1265,
|
|
"status": "ok",
|
|
"timestamp": 1573003281602,
|
|
"user": {
|
|
"displayName": "Anthony So",
|
|
"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCYY-iGjUIqBSnlLoszfZTN7rU7FRNg05Rdt9Ii3A=s64",
|
|
"userId": "11809607246124237079"
|
|
},
|
|
"user_tz": -660
|
|
},
|
|
"id": "EVSGc5Lom2vj",
|
|
"outputId": "28442c3a-4832-4df5-f84d-3d5ef637ed02"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.linear_model import LogisticRegression\n",
|
|
"# Defining the LogisticRegression function\n",
|
|
"bankModel = LogisticRegression()\n",
|
|
"bankModel.fit(X_train, y_train)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 34
|
|
},
|
|
"colab_type": "code",
|
|
"executionInfo": {
|
|
"elapsed": 838,
|
|
"status": "ok",
|
|
"timestamp": 1573003282951,
|
|
"user": {
|
|
"displayName": "Anthony So",
|
|
"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCYY-iGjUIqBSnlLoszfZTN7rU7FRNg05Rdt9Ii3A=s64",
|
|
"userId": "11809607246124237079"
|
|
},
|
|
"user_tz": -660
|
|
},
|
|
"id": "GhFXSRnknr3p",
|
|
"outputId": "e88952b2-01bc-497d-be39-8c2ad85f00bc"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"pred = bankModel.predict(X_test)\n",
|
|
"print('Accuracy of Logistic regression model prediction on test set: {:.2f}'.format(bankModel.score(X_test, y_test)))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 51
|
|
},
|
|
"colab_type": "code",
|
|
"executionInfo": {
|
|
"elapsed": 1127,
|
|
"status": "ok",
|
|
"timestamp": 1573003284105,
|
|
"user": {
|
|
"displayName": "Anthony So",
|
|
"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCYY-iGjUIqBSnlLoszfZTN7rU7FRNg05Rdt9Ii3A=s64",
|
|
"userId": "11809607246124237079"
|
|
},
|
|
"user_tz": -660
|
|
},
|
|
"id": "DVBvXZJknuJ3",
|
|
"outputId": "d865240b-fdbc-4526-bd3f-01b9e42792de"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Confusion Matrix for the model\n",
|
|
"from sklearn.metrics import confusion_matrix\n",
|
|
"confusionMatrix = confusion_matrix(y_test, pred)\n",
|
|
"print(confusionMatrix)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 170
|
|
},
|
|
"colab_type": "code",
|
|
"executionInfo": {
|
|
"elapsed": 1461,
|
|
"status": "ok",
|
|
"timestamp": 1573003287122,
|
|
"user": {
|
|
"displayName": "Anthony So",
|
|
"photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCYY-iGjUIqBSnlLoszfZTN7rU7FRNg05Rdt9Ii3A=s64",
|
|
"userId": "11809607246124237079"
|
|
},
|
|
"user_tz": -660
|
|
},
|
|
"id": "BqcxgdLun0HY",
|
|
"outputId": "2956690b-bee6-42db-826b-670f4c47d5ae"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.metrics import classification_report\n",
|
|
"print(classification_report(y_test, pred))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"collapsed_sections": [],
|
|
"name": "Activity3.02_updated.ipynb",
|
|
"provenance": []
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"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": "3.8.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 1
|
|
}
|