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tensorvijay/random_forest_creditcard_fraud.ipynb

Created October 3, 2023 08:50
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Random_Forest_Creditcard_Fraud.ipynb
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random_forest_creditcard_fraud.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/tensorvijay/ff365171602c127f34659ce8f3b5c6cd/random_forest_creditcard_fraud.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XVbif3K6SXwY"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import average_precision_score\n",
"from sklearn.metrics import recall_score\n",
"from sklearn.metrics import accuracy_score\n",
"import scipy.stats\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MCdIUyJgRxnZ"
},
"outputs": [],
"source": [
"\n",
"\n",
"df=pd.read_csv('/content/drive/MyDrive/Fraud.csv')\n",
"df = df.replace(to_replace={'PAYMENT':1,'TRANSFER':2,'CASH_OUT':3,\n",
" 'CASH_IN':4,'DEBIT':5,'No':0,'Yes':1})\n",
"df.drop(['nameOrig','nameDest','isFlaggedFraud'],axis=1,inplace=True)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 175
},
"id": "LiuYRDCMSpke",
"outputId": "9084aceb-96fd-4d48-9a4b-f272192500e3"
},
"outputs": [
{
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"text/plain": [
" step type amount oldbalanceOrg newbalanceOrig oldbalanceDest \\\n",
"1814542 163 4 131783.46 1544963.45 1676746.92 4624925.7 \n",
"3643655 275 3 86656.57 402013.00 315356.43 0.0 \n",
"6015227 451 3 184559.81 82705.00 0.00 3079590.5 \n",
"1915608 166 3 63102.08 12497.00 0.00 0.0 \n",
"\n",
" newbalanceDest isFraud \n",
"1814542 4493142.24 0 \n",
"3643655 86656.57 0 \n",
"6015227 3264150.31 0 \n",
"1915608 63102.08 0 "
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.sample(4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Ycoo_tWYSqmJ"
},
"outputs": [],
"source": [
"y = df[['isFraud']]\n",
"X = df.drop(['isFraud'],axis=1)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "93fyTPDqS09I"
},
"outputs": [],
"source": [
"train_X, test_X, train_y, test_y = train_test_split(X, y, test_size = 0.2, random_state = 121)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Nd6QkY7AS_nE"
},
"outputs": [],
"source": [
"clf = RandomForestClassifier(n_estimators=50,criterion=\"entropy\",max_depth=10,min_samples_leaf=5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3kLNvs2NTWB9"
},
"outputs": [],
"source": [
"if True:\n",
" probabilities = clf.fit(train_X, train_y.values.ravel()).predict(test_X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zeXcTaJjVAKk",
"outputId": "d7a043f3-674b-4a60-bfed-8bd31d07eab8"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7154750639146131\n"
]
}
],
"source": [
"if True:\n",
" print(average_precision_score(test_y,probabilities))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Pr4NH4xeVT1x"
},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HI4gWqnJWOks",
"outputId": "d82cd37c-50d9-4f44-8469-c10c8041310d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.860777662661556\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"\n",
"print(recall_score(test_y,probabilities, average='macro'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Dcfy7DcpWSd9"
},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xxFzqmOVYnwf",
"outputId": "16c221b4-c6dc-486f-849e-4ea1769c9ca0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9996220110583376\n"
]
}
],
"source": [
"print(accuracy_score(test_y,probabilities))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qIKaweZSY_Xx"
},
"outputs": [],
"source": [
"clf1 = RandomForestClassifier()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4-7hyRWsZIpP"
},
"outputs": [],
"source": [
"if True:\n",
" probabilities = clf1.fit(train_X, train_y.values.ravel()).predict(test_X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BMIHe9OhZMvX",
"outputId": "2b9a4f7e-832c-47bd-cef0-0d9f158111b2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7654222817166213\n"
]
}
],
"source": [
"if True:\n",
" print(average_precision_score(test_y,probabilities))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pFUtxQYMZUM4",
"outputId": "cd6a13ea-0beb-461d-b807-99e937f74649"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8903922206733668\n"
]
}
],
"source": [
"print(recall_score(test_y,probabilities, average='macro'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XKqw5YkhZVBI",
"outputId": "a5f835a1-e789-478c-d088-1a56778ad22f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9996880216011643\n"
]
}
],
"source": [
"print(accuracy_score(test_y,probabilities))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4M_BNbSqGumJ"
},
"source": [
"Let us reduce the number of estimators"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cKcvh6IId6Qb"
},
"outputs": [],
"source": [
"clf1 = RandomForestClassifier(n_estimators=10,criterion=\"entropy\",max_depth=10,min_samples_leaf=5)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zmkUuDeXG-fq"
},
"outputs": [],
"source": [
"if True:\n",
" probabilities = clf1.fit(train_X, train_y.values.ravel()).predict(test_X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8BKyXOprI3yA",
"outputId": "c5ceebc3-1234-42ac-ad66-48fe60fb1dec"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.6929567392759193\n",
"0.8480426630458983\n",
"0.9995921491461065\n"
]
}
],
"source": [
"if True:\n",
" print(average_precision_score(test_y,probabilities))\n",
" print(recall_score(test_y,probabilities, average='macro'))\n",
" print(accuracy_score(test_y,probabilities))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UJb6bxfTJXtS"
},
"source": [
"The ideal number of estimators is around 15, 10 gives you less precision and recall. However, there is an slight increase in accuracy when the estimator size is reduced and deotg and minimum number of samples are adjusted. Further fine tuning will be interesting.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pjX27afxKQd8"
},
"outputs": [],
"source": [
"if True:\n",
" probabilitiest = clf1.fit(train_X, train_y.values.ravel()).predict(train_X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OS0lBiZHOLvL",
"outputId": "aa012486-e00d-4409-d232-ad7613bfd631"
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, ..., 0, 0, 0])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"probabilitiest.predict(train_X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "x3sx3uT1PagO",
"outputId": "50baaf70-4964-404a-bb5a-921ab00f45f9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7431942749256791\n",
"0.8740196443998027\n",
"0.9996703402057643\n"
]
}
],
"source": [
"if True:\n",
" print(average_precision_score(train_y,probabilitiest))\n",
" print(recall_score(train_y,probabilitiest, average='macro'))\n",
" print(accuracy_score(train_y,probabilitiest))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "O9XEgE_EQ63f"
},
"source": [
"Here wer are comparing the performance between the training and the test data set, and we can see that the difference between the raining results and the test results are not particularly high. THis shows that there isn't much overfitting. However, it is highly likely that there is some undefitting since both training and test values are close together, so this is a case of underfitting."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "H1COVTppRIVB"
},
"outputs": [],
"source": [
"clf15 = RandomForestClassifier(n_estimators=15)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QhpEMjc3R04V"
},
"outputs": [],
"source": [
"\n",
" probabilities15 = clf15.fit(train_X, train_y.values.ravel()).predict(test_X)\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0pgto4OLViPk"
},
"source": [
"These are the results for the training data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cvEegDT-VpkC",
"outputId": "13535188-42bc-4353-f8bf-a79ae6eb2a2c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.763744863422387\n",
"0.890686855436983\n",
"0.9996856640817776\n"
]
}
],
"source": [
" print(average_precision_score(test_y,probabilities15))\n",
" print(recall_score(test_y,probabilities15, average='macro'))\n",
" print(accuracy_score(test_y,probabilities15))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "x3szVV9WV7Fb"
},
"outputs": [],
"source": [
"probabilities15t = clf15.fit(train_X, train_y.values.ravel()).predict(train_X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jlNnyCRZWFh5",
"outputId": "27d76ac0-eac1-4b86-cc8c-2fc6dbb481ad"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.98484673816048\n",
"0.9925668530963148\n",
"0.99998055046506\n"
]
}
],
"source": [
" print(average_precision_score(train_y,probabilities15t))\n",
" print(recall_score(train_y,probabilities15t, average='macro'))\n",
" print(accuracy_score(train_y,probabilities15t))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "56Ea137hXtUF"
},
"source": [
"This is a case of overfitting, where the training model has high variance. The training parameters need to be changed to reduce overfitting. So the previous result with n=10 parameters is a much better option than a highly overfitting model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "IYLUN1IBWax4"
},
"outputs": [],
"source": [
"clf12 = RandomForestClassifier(n_estimators=12)\n",
"\n",
"probabilities12 = clf15.fit(train_X, train_y.values.ravel()).predict(test_X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3fHOFgIAZNNE",
"outputId": "e27a1e86-a72b-4284-b950-304ec8b1120a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7692047292921947\n",
"0.8948318076592758\n",
"0.9996927366399376\n"
]
}
],
"source": [
" print(average_precision_score(test_y,probabilities12))\n",
" print(recall_score(test_y,probabilities12, average='macro'))\n",
" print(accuracy_score(test_y,probabilities12))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mJnz__wcaKYt"
},
"outputs": [],
"source": [
"probabilities12 = clf12.fit(train_X, train_y.values.ravel()).predict(train_X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JmunW9RjfiFH",
"outputId": "3a90bf05-2a72-4e0b-e2fe-3e35d7c8ef45"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9761224293704273\n",
"0.9881225070079275\n",
"0.9999693522479733\n"
]
}
],
"source": [
" print(average_precision_score(train_y,probabilities12))\n",
" print(recall_score(train_y,probabilities12, average='macro'))\n",
" print(accuracy_score(train_y,probabilities12))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OS4901l68a0S"
},
"source": [
"Data Analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "bLXBoFVI7iTi",
"outputId": "af38d0ac-38f0-40ea-9d26-0f5bf0bf3f43"
},
"outputs": [
{
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],
"text/plain": [
" step type amount oldbalanceOrg newbalanceOrig oldbalanceDest \\\n",
"0 1 1 9839.64 170136.0 160296.36 0.0 \n",
"1 1 1 1864.28 21249.0 19384.72 0.0 \n",
"2 1 2 181.00 181.0 0.00 0.0 \n",
"3 1 3 181.00 181.0 0.00 21182.0 \n",
"4 1 1 11668.14 41554.0 29885.86 0.0 \n",
"\n",
" newbalanceDest isFraud \n",
"0 0.0 0 \n",
"1 0.0 0 \n",
"2 0.0 1 \n",
"3 0.0 1 \n",
"4 0.0 0 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fgPe1gyBCJaE"
},
"source": [
"From the data analysis in"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9JYpKg_u7mVW",
"outputId": "601419ed-43e7-4ab6-932e-5bd52ff68376"
},
"outputs": [
{
"data": {
"text/plain": [
"PearsonRResult(statistic=0.9988027631729837, pvalue=0.0)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.pearsonr(df.iloc[:,3],df.iloc[:,4] )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fx2m3F19FH87"
},
"source": [
"From above it is clear that the oldbalanceOrg and NewbalanceOrig are highly correlated. For highly correlated data pearson correlation coefficient is 1, this data is highly correlated."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "F6OnlCcEFP2h",
"outputId": "3fc82b6b-a35d-43e5-bb3e-4a5b6a59491b"
},
"outputs": [
{
"data": {
"text/plain": [
"PearsonRResult(statistic=-0.020403492367990984, pvalue=7.526433468566388e-253)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.pearsonr(df.iloc[:,3],df.iloc[:,5] )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TBCNP7-MYyqQ",
"outputId": "1b14cfff-ee22-49a9-8c21-9d17c6efeebc"
},
"outputs": [
{
"data": {
"text/plain": [
"PearsonRResult(statistic=0.9700604740242301, pvalue=0.0)"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.pearsonr(df.iloc[:,6],df.iloc[:,5] )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tqQxGV5mZJhZ"
},
"source": [
"IN additon oldbalacneDest and new balance dest are also showing high correlation"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ia8tPCu3FwnX"
},
"source": [
"However, other combinations show very little correlation"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "M03XZlOFFXiM"
},
"source": [
"Amount and"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "szhJqw1prfPm"
},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 484
},
"id": "bWkcTJui9tIT",
"outputId": "b134ad87-8ee1-4e32-939b-51c3cc471076"
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Fraud')"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"var = df.groupby('type').isFraud.sum()\n",
"fig = plt.figure()\n",
"ax1 = fig.add_subplot(1,1,1)\n",
"var.plot(kind='bar')\n",
"ax1.set_title(\"Which Transaction has more fraud\")\n",
"ax1.set_xlabel('Type of Transaction')\n",
"ax1.set_ylabel('Fraud')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jzuYw4kKIFAG"
},
"source": [
"{'PAYMENT':1,'TRANSFER':2,'CASH_OUT':3,'CASH_IN':4,'DEBIT':5}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VB75WHM1JF8j"
},
"source": [
"It is clear that the Fraudulent transactions are among the Transfer and Cash out transactions. Only two types of transactions are fraudulent"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tMsfmIkgDoTA"
},
"outputs": [],
"source": [
"df2=df.copy()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RQUfyp-oOxpR"
},
"source": [
"Removing all those contributions which do not contribute to a fraud"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0CYK1n_iJ06x"
},
"outputs": [],
"source": [
"df = df.drop(df[df['type'] == 1].index)\n",
"df = df.drop(df[df['type'] == 4].index)\n",
"df = df.drop(df[df['type'] == 5].index)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "ztaVgbkEN677",
"outputId": "558346e5-16cf-4b9e-ad9c-441d548abfef"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div id=\"df-2100021e-3d4c-4a69-816f-95e342820615\" class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>step</th>\n",
" <th>type</th>\n",
" <th>amount</th>\n",
" <th>oldbalanceOrg</th>\n",
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" <td>1281713.04</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>1059852</th>\n",
" <td>119</td>\n",
" <td>2</td>\n",
" <td>2495.37</td>\n",
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" <td>810922.40</td>\n",
" <td>813417.77</td>\n",
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" <tr>\n",
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" <td>2190855.92</td>\n",
" <td>2728658.94</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2680670</th>\n",
" <td>210</td>\n",
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" <tr>\n",
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" <td>0</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2100021e-3d4c-4a69-816f-95e342820615')\"\n",
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" </svg>\n",
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"\n",
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" .colab-df-container {\n",
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" gap: 12px;\n",
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"\n",
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" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
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" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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" }\n",
"\n",
" .colab-df-buttons div {\n",
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" [theme=dark] .colab-df-convert {\n",
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" fill: #D2E3FC;\n",
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"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-2100021e-3d4c-4a69-816f-95e342820615 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-2100021e-3d4c-4a69-816f-95e342820615');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-eefe9c55-aaee-439e-9f9d-a7331935e64d\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eefe9c55-aaee-439e-9f9d-a7331935e64d')\"\n",
" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
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" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
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" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
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" --disabled-fill-color: #666;\n",
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"\n",
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" border: none;\n",
" border-radius: 50%;\n",
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" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
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"\n",
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" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
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" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
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" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
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" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-eefe9c55-aaee-439e-9f9d-a7331935e64d button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
],
"text/plain": [
" step type amount oldbalanceOrg newbalanceOrig oldbalanceDest \\\n",
"5774894 400 2 307161.86 0.0 0.0 974551.18 \n",
"1059852 119 2 2495.37 0.0 0.0 810922.40 \n",
"1126821 131 2 507953.32 0.0 0.0 2190855.92 \n",
"2680670 210 3 147178.76 27590.0 0.0 190691.71 \n",
"3240962 250 3 168555.07 0.0 0.0 378995.71 \n",
"\n",
" newbalanceDest isFraud \n",
"5774894 1281713.04 0 \n",
"1059852 813417.77 0 \n",
"1126821 2728658.94 0 \n",
"2680670 337870.47 0 \n",
"3240962 547550.78 0 "
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.sample(5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Qfdig9RYN8_J"
},
"outputs": [],
"source": [
"new=df.iloc[:,3]+df.iloc[:,4]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NrJxtE2lWhlM"
},
"outputs": [],
"source": [
"df2=df.copy()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "AHeTOKlVWl8l",
"outputId": "3fa83804-40b5-45a5-d041-edcb06fd1fad"
},
"outputs": [
{
"data": {
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" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
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" border-right-color: var(--fill-color);\n",
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" border-color: transparent;\n",
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" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
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" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-2a8717c2-0ed2-402f-bb36-41badb1744c9 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
],
"text/plain": [
" step type amount oldbalanceOrg newbalanceOrig oldbalanceDest \\\n",
"2 1 2 181.00 181.0 0.0 0.0 \n",
"3 1 3 181.00 181.0 0.0 21182.0 \n",
"15 1 3 229133.94 15325.0 0.0 5083.0 \n",
"19 1 2 215310.30 705.0 0.0 22425.0 \n",
"24 1 2 311685.89 10835.0 0.0 6267.0 \n",
"\n",
" newbalanceDest isFraud \n",
"2 0.00 1 \n",
"3 0.00 1 \n",
"15 51513.44 0 \n",
"19 0.00 0 \n",
"24 2719172.89 0 "
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aHF00JItXMCM"
},
"outputs": [],
"source": [
"df2['oldbalanceOrg']=(df2['oldbalanceOrg']+df2['newbalanceOrig'])/2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "XvXbhqMCXkUE",
"outputId": "9d10bcc3-6b91-48f3-f334-e61bf38042eb"
},
"outputs": [
{
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],
"text/plain": [
" step type amount oldbalanceOrg newbalanceOrig oldbalanceDest \\\n",
"2 1 2 181.00 90.5 0.0 0.0 \n",
"3 1 3 181.00 90.5 0.0 21182.0 \n",
"15 1 3 229133.94 7662.5 0.0 5083.0 \n",
"19 1 2 215310.30 352.5 0.0 22425.0 \n",
"24 1 2 311685.89 5417.5 0.0 6267.0 \n",
"\n",
" newbalanceDest isFraud \n",
"2 0.00 1 \n",
"3 0.00 1 \n",
"15 51513.44 0 \n",
"19 0.00 0 \n",
"24 2719172.89 0 "
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-e7z5L7fX8wd"
},
"outputs": [],
"source": [
"df2.drop(['newbalanceOrig'],axis=1,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "iFPN-jQ6YPx0",
"outputId": "8d5d0bb3-f7c4-4d61-f2a7-50ea4b63039e"
},
"outputs": [
{
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" + ' to learn more about interactive tables.';\n",
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" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-9c27649e-cff6-430d-9de8-718c93e2ceaf button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
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" </div>\n"
],
"text/plain": [
" step type amount oldbalanceOrg oldbalanceDest newbalanceDest \\\n",
"2 1 2 181.00 90.5 0.0 0.00 \n",
"3 1 3 181.00 90.5 21182.0 0.00 \n",
"15 1 3 229133.94 7662.5 5083.0 51513.44 \n",
"19 1 2 215310.30 352.5 22425.0 0.00 \n",
"24 1 2 311685.89 5417.5 6267.0 2719172.89 \n",
"\n",
" isFraud \n",
"2 1 \n",
"3 1 \n",
"15 0 \n",
"19 0 \n",
"24 0 "
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9PJiu0hKZc0B"
},
"source": [
"We are averaging all the correlated values and removing the redundant ones"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yJ4_y2jKYRrK"
},
"outputs": [],
"source": [
"df2['oldbalanceDest']=(df2['oldbalanceDest']+df2['newbalanceDest'])/2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "B2ZHCzG0Z3m5",
"outputId": "51d1d6bf-9fb8-44a2-d09c-73bf10282d8e"
},
"outputs": [
{
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],
"text/plain": [
" type amount oldbalanceOrg oldbalanceDest isFraud\n",
"2 2 181.00 90.5 0.000 1\n",
"3 3 181.00 90.5 10591.000 1\n",
"15 3 229133.94 7662.5 28298.220 0\n",
"19 2 215310.30 352.5 11212.500 0\n",
"24 2 311685.89 5417.5 1362719.945 0"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vumRKvchZ4ve"
},
"outputs": [],
"source": [
"df2.drop(['newbalanceDest'],axis=1,inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vPqre8uzbeu6"
},
"source": [
"Actually step is also logically unnecassary"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TPaYuWYabiBC"
},
"outputs": [],
"source": [
"df2.drop(['step'],axis=1,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Y6b9Z-3UcAXy"
},
"outputs": [],
"source": [
"y = df2[['isFraud']]\n",
"X = df2.drop(['isFraud'],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RO7ncKrYcEA8"
},
"outputs": [],
"source": [
"train_X, test_X, train_y, test_y = train_test_split(X, y, test_size = 0.2, random_state = 121)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-lHUZFOKcc_K"
},
"source": [
"We will now apply the lessons learn't from our previous experiments on the raw data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-3pWb1eEcWiV"
},
"outputs": [],
"source": [
"clf1 = RandomForestClassifier(n_estimators=10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8TFs0-hhccKL"
},
"outputs": [],
"source": [
"probabilities = clf1.fit(train_X, train_y.values.ravel()).predict(test_X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Agp3zJ6NdKZK",
"outputId": "21f5a097-1d8f-459b-87e9-39f8775a1e8e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7256710801241542\n",
"0.8768268835457358\n",
"0.9992004793514317\n"
]
}
],
"source": [
" print(average_precision_score(test_y,probabilities))\n",
" print(recall_score(test_y,probabilities, average='macro'))\n",
" print(accuracy_score(test_y,probabilities))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sxOhEq6mdsy6"
},
"source": [
"The results are similar to the ones we applied to the raw data earlier. However, there is a slight improvement and more importantly, computational time is reduced to a considerable extent. So clearly, we have removed unncessary data. Ths is our final result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HaAG7a2rd5zT"
},
"outputs": [],
"source": [
"probabilitiest = clf1.fit(train_X, train_y.values.ravel()).predict(train_X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BCHBB8zuew7P",
"outputId": "5d826387-30bf-4633-d25b-06d935846b93"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9547410553377302\n",
"0.9776808174915346\n",
"0.9998646409126452\n"
]
}
],
"source": [
" print(average_precision_score(train_y,probabilitiest))\n",
" print(recall_score(train_y,probabilitiest, average='macro'))\n",
" print(accuracy_score(train_y,probabilitiest))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Yde-Su7Ie91s"
},
"source": [
"Now we are suffering from a problem of only slight overfitting, in the case of precision, However, in the case of recall the difference between the training and test case is moderate and indicated ambient training (neither overfitting nor underfitting). It is clear that over data cleanup of removing redundant and unncecarry information has impvoed Machine learnign performance to a great extent. Perhaps with further tuning, we can improve the performance!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "X_7gT84nrDVe"
},
"source": [
"1. Data cleaning including missing values, outliers and multi-collinearity.\n",
"\n",
"Missing Values: There were no missing values or 'Nan' hence data cleaning did not take much of an effort. However, we have removed redundant columns: 'step' and 'IsFlaggedFraud'.\n",
"\n",
"Outliers: As indicated by the graph above, only 'Tranfer' abd 'Cash out' has contributes to fraud, so we are removing all other types. This contributes to improvement in machine learning parameters and keep the overall process cogent.\n",
"\n",
"Multicollinearity: The balance transfer columns have shown considerable correlation as we have seen through the Pearson correlation coefficient. We are averaging them and eliminating the redundant column. This has demonstrated computational efficiency and has evidenced some positive changes in training parameters."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uiLYoSHTsQY0"
},
"source": [
"2. Describe your fraud detection model in elaboration.\n",
"\n",
"We use the Random forests ML algorithm, they are ensemble decision trees which use multitude of descision trees during the training phase, they deal with the overfitting charactersistics of basic decision trees. They create a multitude of descision trees and select the descisions through voting.\n",
"\n",
"We are using a random forest algorithm to detect fraud. Random forest algorithms are good with data with unbalanced data. There are many zeros in the data. After several trails and errors of both the input parameters in like number of estimators and error detection index, we have obtained good results with respect to minimal overfitting. We are using a total of 10 estimators. The difference between evaluation metrics on the training data and test data is minimal when we use around 10 estimators. We observe the performance metrics: precision, recall and accuracy are better when we use 'cleaned' data rather than the raw data. However, we note high metric values for training data, which can possible indicate overfitting.\n",
"\n",
"The interesting feature of this algorithm is that the cleaned data is responding much more positively with minimal difference between the train metrics and test metrics for accuracy and Recall. But for precision there is slightly higher difference. We dealt with multicollinearity through averaging, removed the redundant columns and chose the rows wit Transfer and Cash out alsone since they are the only ones that involve fraud."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "F6hdEr7av_ux"
},
"source": [
"3. How did you select variables to be included in the model?\n",
"\n",
"The variables where choosen on the basis of relevance with respect to fraud. We also used basis common sense. This realizaiton happened after working with the raw data. The computational time for Random Forest was very high, this was unnecessary, so, we removed the step, IsFlaggedFraud is a rule based model so is unndecessary. Columns for source and destination for money were highly correlated with each other, so we averaged them to single columns. Types of transactions which wre not relevant to the fraud are excluded."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CrZYQnoZyHyI"
},
"source": [
"4. Demonstrate the performance of the model by using best set of tools.\n",
"\n",
"Accuracy, Precision and Recall are the performance tools that we have choosen. We have proactively carried out several experiments on the Raw data. Although the resuslts are in general not very different for the raw and the cleaned data, overfitting and underfitting are much lesser with the cleaned up data. We find that the difference in recall for test and train prediction involves optimal value. While the difference is slightly higher for the precision. However, high evaluation metrics for training data may possible indicate overfitting."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ToSHZdYczBhe"
},
"source": [
"5. What are the key factors that predict fraudulent customer?\n",
"\n",
"Transfer and Cash out are the modes in which Fraud takes place. Authorities have to be careful with these transactions. Fraud are anomalous transactions that take place. We have demonstrated very clearly as well as it is well published that Random Forests are the best machine Learning algorithms available. Since Random Forests are highly successful, Fraudulent transaction has a highly non linear nature. This means that it is not easy to detect. Random Forests are known to fail when the data contains considerable number of outliers. Frauds are implicit in the data set. The non linear yet implicit nature of frauds, help random forests predict the fradulent customer.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Sf5-1BAcS2rj"
},
"source": [
"6. Do these factors make sense? If yes, How? If not, How not?\n",
"\n",
"These factors make sense if the fraudulent activities are unorganized crimes: hence th non linearity. If it is a syndicate that involves in this crime then there will be a well established pattern and there would be considerable linearity in the data. If the crime is not organized this data does not make sense, if it is unorganized, then it makes a lot of sense. ALso the threat is not internal (Bank Employees), there would have been some linear pattern."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7Q0VE4S1Wmhj"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "WRJClXAPT3lR"
},
"source": [
"What kind of prevention should be adopted while company update its infrastructure?\n",
"\n",
"Since it is clear that there is no established explicit pattern, the best way is to reduce Transfer and Cash out transactions. Debit transactions have not shown much fraud. The key is to establish a pattern, that will take us to the source. Pattern can be established if Banks in crease the documentation in every transaction, more information is needed for better data analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5wJYgydKWiK1"
},
"source": [
"Assuming these actions have been implemented, how would you determine if they work?\n",
"\n",
"If the measures are working, Fraud will leave a detectable trail, a distinct pattern at least if not linearity"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OmMJ5Y9NWnxl"
},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "imYJLUeye5Sw",
"outputId": "68feb3c8-2044-4dd3-f92e-27609d54dbab"
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1+1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oZSdQxg53IMS"
},
"outputs": [],
"source": [
"df5=df2.copy()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HRihJFI-DJwd"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "doFfXYbWqj53"
},
"outputs": [],
"source": [
"df5 = df5.drop(df[df['type'] == 0].index)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5kNM4cxc_qEO"
},
"source": [
"Smaller amounts of transaction show more fraud"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 484
},
"id": "QoYXty3z-vWp",
"outputId": "165ad9c5-c5a7-4e63-8ff6-fdb87c4bfd38"
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Oldbalnce')"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"var = df2.groupby('isFraud').oldbalanceOrg.sum()\n",
"fig = plt.figure()\n",
"ax1 = fig.add_subplot(1,1,1)\n",
"var.plot(kind='bar')\n",
"ax1.set_title(\"quantity\")\n",
"ax1.set_xlabel('IsFraud')\n",
"ax1.set_ylabel('Oldbalnce')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 484
},
"id": "Yb2tyUMv-8sK",
"outputId": "f19daad3-436b-42a4-85ac-74a056aa92bc"
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Oldbalnce')"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"var = df2.groupby('isFraud').oldbalanceDest.sum()\n",
"fig = plt.figure()\n",
"ax1 = fig.add_subplot(1,1,1)\n",
"var.plot(kind='bar')\n",
"ax1.set_title(\"quantity\")\n",
"ax1.set_xlabel('IsFraud')\n",
"ax1.set_ylabel('Oldbalnce')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 484
},
"id": "9hsAe3XqABrG",
"outputId": "8224c575-2ea0-4cd3-b81d-dafd1bf56f1d"
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Oldbalnce')"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"var = df2.groupby('isFraud').amount.sum()\n",
"fig = plt.figure()\n",
"ax1 = fig.add_subplot(1,1,1)\n",
"var.plot(kind='bar')\n",
"ax1.set_title(\"quantity\")\n",
"ax1.set_xlabel('IsFraud')\n",
"ax1.set_ylabel('Oldbalnce')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "1qO4g2KhcOVS"
},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "MPsn7uvxADVX",
"outputId": "d44639c4-2049-467e-8e9e-5d844b9120e0"
},
"outputs": [
{
"data": {
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" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-048b2eb6-d476-4544-86bb-e8480c39dcaa button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
],
"text/plain": [
" type amount oldbalanceOrg oldbalanceDest isFraud\n",
"2 2 181.00 90.5 0.000 1\n",
"3 3 181.00 90.5 10591.000 1\n",
"15 3 229133.94 7662.5 28298.220 0\n",
"19 2 215310.30 352.5 11212.500 0\n",
"24 2 311685.89 5417.5 1362719.945 0"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 323
},
"id": "AZEQiGA2cCIe",
"outputId": "73d95c96-5702-4dae-d6b7-ae85acbb6995"
},
"outputs": [
{
"ename": "ValueError",
"evalue": "ignored",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-131-f902c1760de2>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFraud\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'isFraud'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)\u001b[0m\n\u001b[1;32m 8400\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8401\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 8402\u001b[0;31m return DataFrameGroupBy(\n\u001b[0m\u001b[1;32m 8403\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8404\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated, dropna)\u001b[0m\n\u001b[1;32m 963\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_grouper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 964\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 965\u001b[0;31m grouper, exclusions, obj = get_grouper(\n\u001b[0m\u001b[1;32m 966\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 967\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36mget_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, mutated, validate, dropna)\u001b[0m\n\u001b[1;32m 897\u001b[0m \u001b[0;31m# allow us to passing the actual Grouping as the gpr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 898\u001b[0m ping = (\n\u001b[0;32m--> 899\u001b[0;31m Grouping(\n\u001b[0m\u001b[1;32m 900\u001b[0m \u001b[0mgroup_axis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 901\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, index, grouper, obj, level, sort, observed, in_axis, dropna)\u001b[0m\n\u001b[1;32m 478\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlevel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_orig_grouper\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrouper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 480\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouping_vector\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_grouper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrouper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 481\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_all_grouper\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 482\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36m_convert_grouper\u001b[0;34m(axis, grouper)\u001b[0m\n\u001b[1;32m 941\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCategorical\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 942\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 943\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Grouper and axis must be same length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 945\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Grouper and axis must be same length"
]
}
],
"source": [
"Fraud = df2.groupby(df2[df2['isFraud'] == 1].index)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "E7EI2zC2iFqZ"
},
"outputs": [],
"source": [
"df = df.drop(df[df['type'] == 1].index)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZQWLxEXgdp1s"
},
"outputs": [],
"source": [
"fraud = df2.loc[df2.isFraud == 1]\n",
"nonfraud = df2.loc[df2.isFraud == 0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "uhcgNwpzdqoM",
"outputId": "e1a89ddf-d036-496c-ceca-15ee80b06cbe"
},
"outputs": [
{
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" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-44c378e7-4f24-4eb7-867f-f45bb2c11688\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-44c378e7-4f24-4eb7-867f-f45bb2c11688')\"\n",
" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-44c378e7-4f24-4eb7-867f-f45bb2c11688 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
],
"text/plain": [
" type amount oldbalanceOrg oldbalanceDest isFraud\n",
"2 2 181.0 90.5 0.0 1\n",
"3 3 181.0 90.5 10591.0 1\n",
"251 2 2806.0 1403.0 0.0 1\n",
"252 3 2806.0 1403.0 13101.0 1\n",
"680 2 20128.0 10064.0 0.0 1"
]
},
"execution_count": 135,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fraud.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "gdBjK2KKeJny",
"outputId": "67d64cec-e0d2-4b2a-9c45-a30bf706271a"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div id=\"df-30ee164c-ad2b-4800-ab6f-47b665de1cc6\" class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>type</th>\n",
" <th>amount</th>\n",
" <th>oldbalanceOrg</th>\n",
" <th>oldbalanceDest</th>\n",
" <th>isFraud</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>3</td>\n",
" <td>229133.94</td>\n",
" <td>7662.500</td>\n",
" <td>28298.220</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2</td>\n",
" <td>215310.30</td>\n",
" <td>352.500</td>\n",
" <td>11212.500</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2</td>\n",
" <td>311685.89</td>\n",
" <td>5417.500</td>\n",
" <td>1362719.945</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>3</td>\n",
" <td>110414.71</td>\n",
" <td>13422.705</td>\n",
" <td>145607.580</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>3</td>\n",
" <td>56953.90</td>\n",
" <td>971.010</td>\n",
" <td>67179.590</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30ee164c-ad2b-4800-ab6f-47b665de1cc6')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-30ee164c-ad2b-4800-ab6f-47b665de1cc6 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-30ee164c-ad2b-4800-ab6f-47b665de1cc6');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-27424477-c88d-4075-b2c0-7a6cca4df7b4\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-27424477-c88d-4075-b2c0-7a6cca4df7b4')\"\n",
" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-27424477-c88d-4075-b2c0-7a6cca4df7b4 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
],
"text/plain": [
" type amount oldbalanceOrg oldbalanceDest isFraud\n",
"15 3 229133.94 7662.500 28298.220 0\n",
"19 2 215310.30 352.500 11212.500 0\n",
"24 2 311685.89 5417.500 1362719.945 0\n",
"42 3 110414.71 13422.705 145607.580 0\n",
"47 3 56953.90 971.010 67179.590 0"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nonfraud.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "silH8ybukcS0"
},
"outputs": [],
"source": [
"f=fraud.sample(1000)\n",
"nf=nonfraud.sample(1000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 463
},
"id": "iouoSVn3eNeK",
"outputId": "12703f6b-27ca-466c-fecc-b09fc4c40fdc"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(1,1,1)\n",
"ax.scatter(nf['oldbalanceOrg'],f['oldbalanceOrg'],c='g')\n",
"ax.scatter(nf['amount'],f['amount'],c='r')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vbyEb9E6jn4J"
},
"source": [
"Relationship between fraud amount and non fraud amounts. Shows how largely, the pattern is non linear and invisible. Therefore more doccumentation is necessary to establish more patterns and help with analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T2jltndPriDb"
},
"source": [
"Lest us try some hyperparameter tuning"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2Vsh8pV6jou-"
},
"outputs": [],
"source": [
"clf1 = RandomForestClassifier(n_estimators=7)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "R25uEvCCsCl3"
},
"outputs": [],
"source": [
"probabilities = clf1.fit(train_X, train_y.values.ravel()).predict(test_X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-q4VTnLnsGpk",
"outputId": "4cf77d99-b166-4fe7-cbe2-b925a2b76b6e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7205768043616442\n",
"0.8802368059448755\n",
"0.9991824314812608\n"
]
}
],
"source": [
" print(average_precision_score(test_y,probabilities))\n",
" print(recall_score(test_y,probabilities, average='macro'))\n",
" print(accuracy_score(test_y,probabilities))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UNJVHckdviLT"
},
"outputs": [],
"source": [
"clf1 = RandomForestClassifier(n_estimators=7,max_depth=3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 74
},
"id": "mSSK3RCSwIpj",
"outputId": "d6937439-11e3-473d-fd0e-9544fc435061"
},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=3, n_estimators=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=3, n_estimators=7)</pre></div></div></div></div></div>"
],
"text/plain": [
"RandomForestClassifier(max_depth=3, n_estimators=7)"
]
},
"execution_count": 154,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf1.fit(train_X, train_y.values.ravel())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_9xT3by0yUEH"
},
"outputs": [],
"source": [
"prob=clf1.predict(test_X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VTyarFsEygMY",
"outputId": "93e6fe18-473a-41e9-edfe-47a2bec4211d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5614608394916616\n",
"0.7873847634686285\n",
"0.9987258203659386\n"
]
}
],
"source": [
" print(average_precision_score(test_y,prob))\n",
" print(recall_score(test_y,prob, average='macro'))\n",
" print(accuracy_score(test_y,prob))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Sz6OuoL7y3ss"
},
"source": [
"Maximum depth has brought the precision down"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3ubUZ4o4y06f"
},
"outputs": [],
"source": [
"estimator = clf1.estimators_[5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 606
},
"id": "4Ua-oj-Py2j3",
"outputId": "f87c2199-7982-42ce-86c9-426486b3386e"
},
"outputs": [
{
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6nD7rZDLgX1eNQnpUSL/W68mcrAgsGhsrVLuxWC/Usy/D5TwPpMdWlaHF3Pv34uopCchLCA1SR6fyeIGXt4kFEmTFaLBEQrh0X9w/LxXjksQej5e21cDtEfwBJSIiIiIiIiI6Tdz70WG8/X2NhLmcAs8tzsUbN0zgXO5Hj35xDC0dvc+Gr56WjNwk8Vm7v7VbxWa4Q+176vF68dLmCqHarFgtliwcGdB+Hjg3C+OSw4VqX9pc6bd5JM9zIiIiIiIiIiIiIiIiIiIiIqJTMdiViIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhOKwabS7h2bFIovr5rIm6cnhjAjvzP6fbii0MtQrVRWiUevyCwYYddPTAvVbh2Y5G+T2tsKW0Xrr1jdjKUCrGw1f5KjQzBonETzGYAAAAgAElEQVRiAadeL7Cj3NjntWxOD/69s16o9taZSTgjO7LPa4l47PwMiGTaerzAN30M9O1qOJzngbTiYAtWH23ttUanluOR+elB6qh7m0vaUam3CdX+aWEWtKrA/mm0Ui7Dny7KEqqtMziwoVj8WkVEREREREREdDowWCXM5ZLD8M0vZuDGmeIz46FuRX4DVh9u6rVGp1bg0QtygtRR94yC89ehFuK5uagNlW1Wodo/XTwaWpUioP0o5TI8ecloodo6gw0bCnuf+YrieU5EREREREREREREREREREREdCoGuxIRERERERERERERERERERERERERERERERER0ZD0s1lJWH3HeIyK1w50K5LtrDRCbxHbWPvG6YmI0QVvc/UFY2KQHasRqm2zuFDeKhba2NXuSpNw7cKxMZKP3x8XjokWrt1d1fdg12V7G9Fqdvqsiw1V4TfnZ/R5HVE5sZpew2O1Kjkmp4bhuqkJSI1UB7yf407n8zxQGowOLFlT7rPu3jNTkRgevO9Vd97/oVGobkpaGM4bLX7u9cfszAjhoOSluxsC3A0RERERERER0eD0szPSsebeGRiVEDrQrQwaDUY7Hl9Z6LPu3nmZSIwICUJHPRMN9ozQqALcSXC9t7tWqG5qegTOz40LcDedZmdH4cwcsdnnuztrAtzNiXieExEREREREREREREREREREdFwEry/1CYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIgqCGJ0SLyweifODFAIYCDvKDMK1V06KD2Anp5LJgLNHRKG8VSzQ8EBdh3AQ7HHFzVahuoRwNTKjpR27v2ZkRAjXlrRID7U97m3BwMj756YiVK3o8zpSXDUpHjvKDUiPCkFeYijyEnUYm6RDXmIosmI0kMuC0gaAoXGeB8qvvyz1GcyQHKHG3WemBKmj7jWZHFhfpBeqvfes1AB3c/J6Kfiu3Pd1eEtpO2ra7UiLGtggDiIiIiIiIiKiYIkJVeGFK8figiCFXp5OfvVZge+5XGQI7pmXEaSOema0OYXqIrRDZ6vCRpMd64+1CNXee3ZmgLs5db0dZb5npVuK21DTbkNaVGA/F+F5TkRERERERERERERERERERETD0dD5bSkiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhr2zsqJxEuXj0RCuHqgW+mXnZVGobrxyaEYFa8NcDenOiM7Au8IBo9W6e2Sj1+pFwtEnZwSKvnY/ZUSqUZcqAotZt8b31cL3o+T7a02obzV923jQlW4ZUZin9boi8smxGHh2JigBcn2ZKic54Hw/g+N2FTS7rPudwsyoVXJg9BRz749pofb4/VZF6NTYkGQA3zPHhGFpAg1GoyOXus8XuDrgjbcMSc5SJ0REREREREREQ2cs0bE4KVrxiIxPGSgWxl03ttVi01FrT7rfr9wFLSqgZ2vAoDRRwDtcZGaobNV4bdHW8TmkaEqXJAbH4SO/ufsUTFIighBg7H3z1M8Xi++PtKMO85MD1gvPM+JiIiIiIiIiIiIiIiIiIiIaLgaOr8tRUREREREREREREREREREREREREREREQB5/Z4UdxsxcE6M8parShvs6HB6ECbxYl2qws2lwcOtxcejxdKhRxqhQxhIQpEaZWID1UhLSoEmTEajE3UYUJy6GkRxFTWasOB2g6UtlpR1mpDo9GBVosTbRYX7C4P7C4PXB4vVAo5QpQyhKoViA9VIT5MjayYEIxJ0GF8cijGJ4dCKZcN9N3ptzaLC7sqjciv7UB+bQcaTQ4YbW4YbS44PV6EqRX//Z6PjtciLzEUE1NCMTsrIiD3/1ijBftqOnCgrgNHGsxot7r+2w8AaFRyRGmUSI5QIytWg0kpYZidGYHcRJ3fexkIDpcHu6pMyK/tQGlL589oc4cDFocHZocbDrcXWpUcOrUCESEKZMVoMCJOi7GJOswbEXlanIPBYrS5cKjejAO1ZhQ1W1BrcKDOYIfR5oLV2Xmuq5Vy6H58PMNDFMiM1iArVoPsGA2mpoUhLzEUskF8mg/HazgNL0q5DL+en477zkrFEHjKRWGTVahuWnp4gDvp3pRU8XVrDdKDXTvsbqG6uDCV5GP7Q6xgsKvBJnY/TvZJfrNQ3bVTE6BWBi+cU6mQQakYuNCBoXae+1uV3o4/fVvps27+qGhcOiEuCB317ttjbUJ1iyfGQ6kI7jdcLgOumBiPV7bX+qz9hsGuRERE5CdujxdFTWYcrDWhrMWCilYL6o12tJmdaLc6YXN64HR74PZ0vjbvnN0oEa1TIS5MjbQoDbJitchLCsOE1PDTIoiprMWCAzVGlLZYUNpiQaPRjlazE21mJ+wuN+wuL1weD1QKOTTKzrlcfJgaCeFqZMbqkJsYinHJ4ZiQGj405u9mJ3ZVtGN/jRH51UY0mjrnk0arCy6PB6EhSoSpFYjSqTA6IRRjk8MwMTUcs7OjA3L/Cxo6sL/aiPwaI47Wm6C3dvZyfP6uVckRqVUiOVKD7FgtJqZGYHZ2FPKSwvzey0BwuDzYVdGO/BojSps7f0abTQ5YnG6Y7W443B5oVXKEqpUI1yiRHavFiHgd8pLCMG9UzGlxDgaL0ebCwVoTDtYaUdRoRm27DbUGW+f83XF8/i6DTq2ATqVAuEaJzBgtsmO1yIrVYVpGJPKSwgb9/H24XcNpeFHKZXj4ghzcd3Ym5IP5ZBwgVXor/rSm2GfdeWNicemkxCB05JvBJhbsGj6kgl3F5u6LJyVBFfR5pAxXTEnCK1t8z3e/PtIUkGBXnudERERERERERERERERERERENNwNnd+WIiIiIiIiIiIiIiIiIiIiIiIiIiIiIqKAaLO4sOZoK9YV6vF9hRFmh1g4lcPlgcPVGcrVYHTgWDc12bEazM2JxKKxsZiTFQHFINh43er0YGOxHmuOtmF7mUEosAv43/012TrvL2A+4ethIQrMyozAT8fFYuHYGISqAxuIdbjejAtfO+iz7v65qXjs/Ixea7xeYEOxHh/ubcL6Yj1cbm+Pte1WF9qtLtS023G43gygBQAQo1PiorGxuG1WEnIT+heq2mhy4JP8ZizPb0ZJS++hd063GyabG9XtduyuMuGT/Z2btWZEh+DaKQm4aUYSYnSB//OKd3Y34PHV5T7r7j0rBY9fkOmzblNJO5bubsD2cgMsDk+vtR12NzrsbjSZgJIWK9YX6f/7tbxEHa6ZkoBrpyQgXDNwIW0DpbjZiq+OtGJjcTvya03w9PyjDQCwOT2wOT1os3RudFzQaDnh69E6JeZkRuCyiXG4cExM0APIujPcruE0fGVGa/DqVaMwOXVoBIc0dTj/G5Liy9gBCiuPCxV//hQNaT3O5fHC5eui/KMo7cD8maToujZX78/T3XG5vVh1pNVnnUwG3DBtcIQABMNQO8/9zeMF/u/zEp/P9VFaJf52aU6QuupZh92N7eUGodoLxkQHuJvunTc6SijYdU+1CW0WV1DeVxAREdHQ02Z2YvXhJqw/1oLvyvQSZjfeLrMbe7c12XE6zBsZg0Xj4zEnO3pQzG6sTjc2FrZi9eEmbC/Vo6XDIXS7zlmVB0abq8v9/d/7prAQBWZnR+OSCQm4aHxC4OfvdSYseGm3z7r7z8nCby4c0WuN1wusL2zBh3vqsKGwBc7e5u8WJ9otTtS023C4zoQV+Z3/HhOqwkXjEvCzM9KQm9i/90yNRjs+2VePT/c1oKTZ3Gut0935PanW27C7oh0f760HAGREa3Ht9GTcPCsNMaGqfvUj4u3va/D4ykKfdffOy8SShSN91m0qasW7O2uwvVQPi49z8vj8vdFkR0mzGeu6DFLzksJwzbRkXDs9BRFDKBRPVHGTGasONWFTUSv2Vxvh8fY+67E5vZ3zd3R+LlfQ0HHC16N1KszJjsZlkxNxYV580AP3ujPcruE0fGXGaPHqteMxJT1ioFsZlDxeL3756VHfczmdCn+9Ii9IXflmtIp9BhE5QDN4f+uwu7G9VO+7EMCCvLgAd9O983PjhIJd91Qa0GZ2+vV1Fs9zIiIiIiIiIiIiIiIiIiIiIiIGuxIRERERERERERERERERERERERERERFRDw7WmfHqjlp8U9DW60bi/VHeakN5qw1L9zQiKUKNm6cn4paZSQMS0FWlt+M/u+rx0f4mmGzSwsdEdNjd2FCkx4YiPR77So4bpiXi7jNTkByh9vtaABAeIrZxvcFHaNz+mg4sWVOO/NqOXut8abO48P4PjfhwXxOunhyPx87PQJzEjUZNdjde2VaLN76vh70PIWldVenteG5jNV7ZXoeHzknD7XOSoQzgptiim7Y3dfQeJLyxWI9n1lfjSEPvG+qLKmi04I/fVOD5jdX42awkPHh2GrQquV+OPVh5vMCXh1qwdE8DdleZ/HpsvcWFNQVtWFPQhrhQFa6dmoB7zkwZkGvacLuG0/C2eGIcnr44R/i573SQEKZCxe9nQ29xos3iQtuP/6vv+t9WF9rMTkxLDx+QHtVKOXRquc+AcQCwOqU9byvlMmhVcqHbhSgH5nlL9Pkyog8/l7urjDAIbKo/JTUcGdEhko9/OhqK57m/vfl9HXZWGn3WPbUoGwnhgXkPIsXWMoPQaxSdWo5ZGQNznZuWHo5wjcLn+0O3x4uNxXpcOSk+SJ0RERHRUHCw1oRXt1bi6yPNcLr7N+vsSXmLBeUtFry7swZJESG4eVYqbp2dhihd4IM2T1alt+KtHdX4eG89jD5m0n3RYXdj/bEWrD/Wgse+KMQNM1Nwz9xMJEcG5j1TWIjY/MvXfd1XbcTvVhVif7Xv1/K9aTM78f7uWnz4Qx2unpqM31w4AnFh0l73m+wuvLy5Em9sr/LD/N2K59aV4ZUtlXjo/BzccWZ6QOfvooFzzT6ChDcUtuKZb0txpN4/c+OChg78cXUxnl9Xhp+dkY5fzs+CVjW039d6vF58eaAR7+6qxe6Kdr8eW29xYs2RJqw50oS4MDWunZ6Ce+dmDMg1bbhdw2l4Wzw5Cc9cNgbhgs99w9Eb26uxs9z3Ne8vPx2DxPDBM8/19XsCxw2VcPKtJa1C12ydWoFZWVFB6OhU0zIiEaFR+nwN6fZ4sbGoFVdOSfLLujzPiYiIiIiIiIiIiIiIiIiIiIg68TdoiIiIiIiIiIiIiIiIiIiIiIiIiIiIiOgEdQYHfv91Ob4uaAvqug1GB57bWI1/fVeHX5yVirvPTAnoRt/HGW0u/HVTDZbuaQhY+OHJrE4P/r2zHkt/aMSD81Jx31mpUCn8e19Fw8bMPYSxebzAU+sq8fp3dfD68WFxe7z4cF8TtpS2Y+kNechL1AndbmupAb/4rBit5t6DT6UyO9x4cm0lNhS3442rRyNaF5g/tQhVi30/9JbuN2k1O9x44ptKLNvb6M+2Tjj+S9tqseJgC56/NAdnjxiYzWoDbWOxHk+trcKxJkvA12oxO/Hytlos3dN5nv98drLfz/PuDLdrOA1voWoFnlqUjasmD80gPZVChoRw9aAIYOxJhEYJi6P3UBRA/HVJV1FaJaxO38f2FbgYKB12sXX78tpiU4lY8MnCvBjJxz7dDPXz3F+Km614dkO1z7qLx8XisglxQejIt+/LDUJ1szIioB6gAGelXIY5mRFYW6j3Wft9hZHBrkRERCSkzmDD71YV4esjzUFdt8Fox3PryvDatir84uxM3D0vM2jz9+fXlWHprtqAhR+ezOp04987qrF0Vy0ePDcLvzg7EyqFf19TagXnvR327ue9Hq8XT31dgte2V/l//v5DHbYUt+G9WychLylM6HZbi9tw38dH0Gr2/T5cCrPDjSfXFGPjsRa8ccMERAcokDJULRaWqrd0//mC2eHGH78qxrI9tf5s64Tjv7S5AivyG/DXy/Nw9qih+X5+Q2Ernvq6BMcaOwK+VkuHAy9vrsDSnTV4cH42bj8jze/neXeG2zWchrdQtQJPXToGV09NHuhWBrXiJjOeXVvqs+6SCQm4bFJiEDoSZ7SKfe4eqR0agdLflYnN3WdlRQ3oPHJ2dhTWFrT4rP2uTN/vYFee50REREREREREREREREREREREJxqY3xwiIiIiIiIiIiIiIiIiIiIiIiIiIiIiokFp+YFmnPtKftADAbsy2dx4en0VLnr9EMpabQFda1uZAee+cgBv7awPWqhrVw6XB89vrMZFbxxCdbvdr8fWCm5kbu4mlMzscOO2D47htR3+DXXtqs7gwGVvHcbeapPP2pe31eKG9476PdS1q+/KDbjynSNot3a/0X5/aVVi3w+T7dT1mzqcuOTNwwELde2q1mDHje8V4MWtgdnAfqAYbS784rNi3PT+saCEup689pNrK7HojUMoabEGdK3hdg2n4W1SahjW3jORYY8DzOYUC+WJ0koPN40NFdssvq2HUJZAMwkGu8b0IbRmU7HYBvMX5kZLPvbphOe5GJfHiwdXlMDu6v18jA9T4emLc4LUlW97qny/DwCAKWliQVSBMiUtXKhuV6UxwJ0QERHRULB8fwPOeWFn0AMBuzLaXPjLt6VY+PIelLUEdk62raQN57ywE299Vx20UNeuHC4Pnl9XhoWv7EG13r9zKl1/5+9LD+Jf2/wb6tpVncGGS1/7AXurDD5rX95cgevfzvd7qGtXO8r0uPLNfWgP0Ht40fm7sbv5u8mBi1/dE7BQ165q22244e18vLipIuBrBZPR5sJ9Hx/BTe/kByXU9eS1n1xTjIte+QElzeaArjXcruE0vE1OC8e6B2Yx7NEHl8eLBz49KjCXU+PpS3OD1JU4QzfPi90J14g9zw52eyrE5u5T0yMC3EnvpqRHCtXtErw/PeF5TkRERERERERERERERERERER0Kga7EhEREREREREREREREREREREREREREREA4Jn1VXhwRQk6BEOqAu1IgxkXv3kI31cEJiDnP7sacMN7BWgwBm6zclFHG8xY+PpB5Nf6b8NrrVLsTwbMjhO/326PF3d9XIT1RXq/9dKTDrsbd3xchOaOnjdzf3ZDFZ5eXwVPEHJ3jzVacPtHhQFZS6MS+34YTzr/GowOXPGfwygMYhipx9v5uP92dXnQ1gykw/VmnPfqAXx+sGVA+zjSYMZPXjuIFQHqY7hdw4l+tyATWTGagW5jWPN6gQ6H2DWnL8Gu45JCheqONQU2NLs7Xi9Q3S4WCjQ2SSfp2C1mJwoafT/vJ4SpMCJOK+nYpxue52Je3FqLA3W+30c899MRiNFJPxcDweHyoEDw9e3k1IENdhVdv6LNBpNtcLwOIyIiosHp6W9L8cAnRwbP7KbehEWv7sH35YGZA//nu2pc/3Y+Goz2gBxfiqP1HfjJy7uRX+O/OZVGKRjs2s38/c5lh7DuWOBnlR12N25fdgjNHT1/BvLM2lL85dtSeAKVMNtFQUMHbl92KCBric7fTScF2DUY7bj8jb0obAxsIGhXHq8Xz6wtxW+/LAzamoF0uM6E+f/Yhc/zGwa0jyP1Jlz40h6sCFAfw+0aTvS7i0YhK3Zozx794Z+bKnBA4PXF85fnISZUFYSOpDFaxYJdIzWDr3epHC6PcPj4pLQBDnYVXL+i1dJtaL0onudERERERERERERERERERERERKcaHH+BSUREREREREREREREREREREREREREREQD6tkNVXhpW+1At3EKg9WFW5Ydw0e35GFqWrjfjvvK9lr8ZV2V347nD3qLC9e/V4Dlt47FWMEgs94oFTLIZJ3BY72xOD0n/P9L1pRjU0l7v9cX1Why4IEVxfjw5rGnfO3fO+vx4tbg/lx+X2HEazvqcO9ZKX49rlohE6rruim4w+XBzcuOoaxVLDjO397d3YAwtQK/vSBjQNb3h62lBtzxceGg2Wzd6vTggRXFMFhduG1Wkt+OO9yu4YG2qaQdN75XMNBtBNTts5PxxMKsgW6DTnPV7Xa43GJhLKmRIZKPPz09HB/vb/JZV9RsgcPlgVow1N4fGkwOWBwe34UAZmZI2wR+f43Y5vKzsgZ2c3kaHA7Vm/HPrTU+666eEo8FY6KD0JGYY01W4evHhOT+vzfqD9H1vV7gUH0HzsiODHBHREREdDp6dm0pXtpcMdBtnMJgdeHmdw7go59PwbQM/72OeWVLJZ76psRvx/MHvcWJ6/6zH5/dMQ1jk8P6fTyV6Pz9pGDXJauKsKmotd/ri2o02nH/J0fw0c+mnPK1f++oxoubKoLWCwB8V6bHa9uqcO+8TL8eV3Qm0DXY1eHy4KZ3DqCsxeLXXkS9s7MGoSEKPP6TkQOyvj9sLW7D7csODqL5uxv3f3Kkc/4+J81vxx1u1/BA21TUihvezh/oNgLq9jPT8aeLRw90GxRgh+pMeHFTuc+6a6YlY0FeXBA6kk40FDRCe/pvVXis0Qyn6DwydWA/0xRd3+vt/Dk8M2fwzH2JiIiIiIiIiIiIiIiIiIiIiE53wftLZSIiIiIiIiIiIiIiIiIiIiIiIiIiIiIalL460hr08EwpzA43fv5REZo7nH453ucHWwZdqOtxBqsLt31YiHar2CaqvijlvsNErV2CXVcfbcXSPY1+WVuKraUGbC01nPBvu6tMeOKbiqD3AgD/3FqDFrN/ft6O04huLN9lA/Q/fFOBIw1mv/Yh1Svba7H6aPCCBvxpa6kBNy0rGDSbyh/n9XYGKL+1s94vxxtu13AiGjykPEdNS5cemjMzU2zzcpfbiz3VJsnH748DdWLhqwAwI0PaJvCix56RfvoEZlNgOFwePLiixGdAakqkGn/6SXaQuhJT1CwWnBQWokBCuDrA3fQuWqdElGB4RVGzNcDdEBER0elo1aEm/DPI4ZlSmB1u3P7+ITR3OPxyvM/zGwZdqOtxBqsLty49gHaLf+ZUSrnvma/V+b/Z5FeHm/Duzhq/rC3F1uI2bC1uO+Hfdle044+ri4PeCwD8Y2M5Wvz083ZciPD8/X+fvfxhdTGO1Ad3nnCyV7ZU4qvDTQPaQ19tLW7DTe/mD8r5++MrC/HWd9V+Od5wu4YTkRiHy4MHPjniMyg0JVKDJwZxyG+7Vew1UYTm9A92LWoS+zwjLESBxPCQAHfTu2idClE6lVBtUePA/i4BEREREREREREREREREREREdFQc/r/thQRERERERERERERERERERERERERERER9Vmr2YmHV5ZJvp1GJcesjAjMygxHVowGmTEaJIaroVHKoVXJoVLIYHN5YHV60NzhRE27HeWtNvxQbcLuKqPkgL8mkwOPrCzF29fnSu61q/JWGx5dJf3+AkBugg5nj4zCuCQdMqI1SIlUI1StgFYlh0Iug8nmhtHuQpvZhYN1Hciv68DWUgMajNI2U65pt+ORlWV445r+b/KqUsh8bihrcXRuut1mceE3X5X3WBejU2LRuFjMzYnEmAQdEsPV0KrksDjc0FtdaOlw4odqE3ZWGLG51ACHy9Pjsbrz7IYqzBsxAUBnuOkDnxXD00vrE1NCsWBMDGZmhiMnVosorRIquQwGmwvtVheq2+3YVWnEtjID9teIh68BQIfdjde/q8PjF2RKul1vlArfIbvA/4J2Nxa3+wzZjdIqMT09HDMzwzElNQyxoSpE61SI1irh9njR4XCjpt2OwiYLtpQasLFYD5NN+ibrv/qyFJNSwpAWNbCb2EpxtMGMOz4u9Bl01p1QtQJzsiIwNS0MU9LCkRYVgmitEhEaBRxuL0x2N+qNdpS22JBf24FNxXqUtdokr/PEt5UYEafFOSOjJN/2uOF2DSeiwWV9kV6oLlyjwKh4neTjj4zTYlxSqFCA7IqDLTgzO1LyGn219pjYfZ+REY7kCGmhlAdqxTZiH5sUKum4NPQ8t7EahU29B6TKZMDfLxuJcI0iSF2JKW0RC0DNjtEEuBMx2bEaofcUxQx2JSIiopO0mh14eEWB5NtpVHLMyorCrKwoZMfqkBmrRWJ4CDQqObQqxf9mNw43mjscqNHbUNZqwQ+VBuyuaJcc8NdosuPhFQV45+ZJknvtqrzFgkc+P9an2+YmhuGc0TEYlxyOjBgNUiI1nfN3teLH+bsLRpsLrWYnDtYacaDGiC3FbWgw2iWtU9Nuw8OfH8ObN0zoU59ddc7fe6/57/zd7MRvvijssS4mVIWLxyfgrJExyE0MRWJECLQqRef83eJES4cDeyoN2Fmux+biNunz93WlmDcqBkBnuOn9nxyBx9vz7HRiajgW5MVjZlYURsTpEKVTQimXw2hzot3iQrXeip0V7dhe0oZ91UZJvXTY3XhtWxWWLBwp6Xa9UUmdvxe2+gzZjdKpMCMjEjOzojA5PQJxoerO+btOCbcH6LC7UK23oajJjM3FrdhU2AqjzdXrMbvz688KMDktAmlRg+P9j4ij9R24fdlBn58/dSdUrcAZOdGYkh6BqemRSIvWIFqnQoRGCYfbA5PNjXqDDaUtFuyvNmJTUSvKWnp/79udP64uxog4Hc4ZHSv5tscNt2s4EYl7bl0ZCn0EaspkwAtX5g3qUFSj1ffzlkzWGXZ6uitpFpu7Z8dK/ywjEHJitdhn8f15rOj9IiIiIiIiIiIiIiIiIiIiIiIiMYP3N76IiIiIiIiIiIiIiIiIiIiIiIiIiIiIKOBe2FIjaaPp3AQdfjE3FReNjUGIUt5rbahagVC1AnGhKuQldm6AeSeS4fUC28oMWLa3EauPtqKXvcNPsLZQj80l7f0KQVyyphxmh3iwpUIuw2UT4vDLs9OQE9v7ptrROiWidUpkRgNT0sJwCwCXx4tvCtrw6vY6HKgTDxddfbQVW0sNmDeifwFlSrkcQO8bvNt+3AD+iW8r0Go+dXPQhDAVfj0/HVdNioe6m+95hEaJCI0SmdEaTEsPx11npKDF7MQz66vw4b4m4V7zaztwqN6MCcmh+Mu6SlS3d78h/7kjo/DIeRmYmNJ9mFlsqAqxoar/BmY+eh5wsM6M3/MBqAIAACAASURBVHxVhvxa8e/BR/ua8Mj8DOEN4X1RysWO43B54PJ48eS3FT3WTE8Px31zU3HeqCgoejiuSiGDRiVHXKgKk1PDcM2UBJgdbrz/QyNe2FIjKeDVZHPjybWVeP3q/ocNB4PJ5satHxSiwy4txDYvUYd7z0rFRXkx0Ki6v75p5TJoVXIkhKkwKSUMl0+MAxZm4WiDGf/e2YDlB5rh7i2RuAu3x4t7Pi3Gunsm9jk0d7hdw4lo8Oiwu/F1QZtQ7cyMCAg+DZ7i5hmJeHSV7wDrFQdb8NA5aUiNDHwIeavZiVVHWoRqb5yeKPn4h+rFXq/kJgyODeZpYOypMuH17+p81t0yIwlzc4IXeiyqvM0mVJft4z1YsOTEaoWCXSsE7xcRERENH3/fUC5tdpMYhvvPycRF4xPEZzdhauQlhQEA7joLnbObkjYs21OLrw43ic9uClqwuai1XyGIj68qkj5/n5SI/5ufjZy43t/jdAZqqpAZo8XU9AgAnfP3r48049WtlThQIx4uuvpwE7YWt/036LSvRGa+th+DRJ9YU4xW86lhjQnhajx8QQ6umpLc+/w9RotpGZG4e24GWjocePrbUnz4g+/3BMftrzbiUJ0JE1LC8ZdvSlGt7/6167mjY/HoghGYmBre7ddjQ9WIDVVjRPyPgZkLRuBgrQmPfXEM+RK+Bx/trcOjC3KgUvT+cy6q87MQ347P3/+0prjHmumZkfjF2Vk4b0xsL/N3QKNSIy5MjSnpEbhmWjLMDjfe21WLf2yUdt4bbS48uaYYr1/f/7DhYDDaXLhl6QHp8/ekMNx3diYuGpfQy/xdAa1KgYRwNSalReDyyUkAOoNk39xRheX7GyTN3+/+8DDWPzirz6G5w+0aTkRi9lS247VtVT7rbp2dhrkj+/daI5DcHq/Q67bwECXkMv98Xj6QKlqtQnXZPl6TBkt2nA77qn2/tioXvF9ERERERERERERERERERERERCTGP7/dTERERERERERERERERERERERERERERESnHaPNJRy8KZcBj56XgfX3TsLiiXE+NyTujUwGzBsRidevHo1Vt0/AuKTuAzq7848tNX1ed0e5AZtL2oXrY0NVWH7bOLx4+Uifoa49UcpluHhcLFbeMR73npUCKXuePr2+sk9rnri+7xqHy4PD9WZ8dqD5lK8tGhuLLfdPxg3TErvdVL4ncaEq/PXSEXhqUbaUdvHVkVYUNlmwbO+pP5c6tRz/vHwk3r8pr8dQ155MTAnF5z8bhwtzxTfPbbO4sK1M/OfFFynf+6V7GlHUfOomrCFKOZ7/6Qh88fPxWDAmusdN5XsSqlbgrjNSsOm+yZiZ0f3G/D356kgr9lSZJN1moCxZU45aQ/fBwN0JC1HgmYtzsPaeSbh8YlyPm8r3ZmxSKP5+2Qisu2ciJqeGCd/OaHPhN1/5Dizs6bbD6RpORIPLWzvrYbCKBVtcOr7vgRKLJ8YhQqP0WedwebBkdXmf15HiuY3VsDg8PutidEpcMk7afTfZ3GjucPqsSwhTIVrn+3Ghocni8OCXn5fAV5ZNVowGSxZkBqcpiWraxV6rJUcEPqxZRFK4WqiuqodwLCIiIhqejDaXcPCmXCbDYwtGYMODs7B4clL/ZzejYvD69ROw6p4ZGJcsPgd8YWNFn9fdXqrH5qJW4frYUDU+u2MqXrp6nM9Q154o5TJcMiEBq+6ZjnvnZUqawf7l25I+rXnC+grfCzrcHhyuM2H5/vpTvrZofAK2PjQHN8xIlTZ/D1Pjb1fk4amfjpHU76pDTShsNOP93bWnfE2nVuDFq8dh2W2Tewx17cnE1HB8cdc0XDg2Xvg2bWYntpboJa3TG0nz9501KGoyn/LvIUo5/np5Hr68azoW5MX1af5+99wMbPrlbMzMipJ021WHmrCn0n+fRwTSkpWFqG0Xf+8TFqLAM5flYt0DM3H55KS+zd+Tw/DClWOx/oFZmPJjsLMIo82Fx744Jnm947cdTtdwIhJjcbjx4KdH4fGRupwVq8WShSOD1FXfiAZXi8znTwc1erEA1JTIQTKPFJyLVrUx2JWIiIiIiIiIiIiIiIiIiIiIyJ+Gxm9MEREREREREREREREREREREREREREREZFkKw+3wub0HUoFAH+9dASumZLg9x6mpIVh5e3jcdcnRVhf5HsT7z1VJhyqN2NCsrRgTwB4dbvYBswAEK1T4qs7JiAj2j8bdyrlMjx+QSayY7R4eGWp0G0O1pnxfYURc7LEN6k+mUxgN3OH24s/flOBk/efvWNOMv5wYZakDdFPduvMJJS12vDWzlM3re/OqsOt2F/TAfdJKVWRWiU+uClPUmDmydRKOV69chQueuMQCpssQrdZW6jH/FHRfV6zK7mEB/KJbytO+bcIjRIf3ty/x+C45Ag1PrplLO74uAgbBM674/6xpQbLbsrr9/qBtL5Ij+XdhBT3ZGScFm9fn9vn8OaTjUnQ4cufj8dvV5dj2d5GodtsLG7Hl4dacOmEOElrDbdrOBENHiUtVvxz66khMN2JD1PhYonhpl2FqhV4ZH46lqzxHdq6tlCP13bU4e4zU/q8ni9fF7Th/R/Eru+PnZ8hOcijQjAUMi1K2vOWxeHBdxUGHKk3o7DZipJmK9qtLpjsblgcbigVMmhVckSEKJEWFYKM6BCMTQrFjIxwjE3USQ6zocB6cm0FKtp6/1mRy4B/Xj4S2j4E5gRDncEhVJcQpgpwJ2LiBfuoFbxfRERENDx8eaBReHbztyvycM20ZL/3MDU9AqvumY67PjiEdcdafNbvqWzHoToTJqRIC/YEgFe3VgrXRutUWH3fdGREayWv0x2lXIYlC0ciJ06HX68oELrNwVoTvi/XY0523+e/MgjM310e/OGr4lPm73eelYE/XDSqX/P32+akoazFgre+qxaqX3WwEfnVhu7n77dNkRSYeTK1Uo5/XTseC1/ZjcLGU0NTu7OuoBnnjen7zKArSfP3NcWn/FuERokPf9a/x+C45MgQfPzzKbhj2SGsFzjvjnthYwU+uG1yv9cPpHXHWrB8f4Nw/cj4ULxz88Q+hzefbExiKL64azp++2Uhlu0Rm01tLGzFlwcacemkRElrDbdrOBGJefLrElS09h6kKZfJ8OJV46BVKYLUVd8YrILBrlrf2xTWtNuwr8qAg7UmlLZYUG+wodHkgNXhhtXpgcfrhVYlh06tQLROhfRoLdKjNRibHIZpGZEYnRAq6bm8L2oNdqG6+PDBEeyaINiHlLB1IiIiIiIiIiIiIiIiIiIiIiLyjcGuRERERERERERERERERERERERERERERMPUxuJ2obpLxscGJBDwOI1KjreuG4Nr3jmKnZVGn/VfHGqRHApYa7BjS6nY/QWAv1820m+hrl1dPy0B+2tN+GBvk1D9p/nN/Qp2Fcng6rC78X3FiY/7VZPj8cefZPV53a4eOy8DXx5qQYvZ6bO2Um9D5UmBZiqFDP+5boxfAk01Kjn+fFEWrnrnqFD9zgrfP4+ipGxF63KfuLF+iFKO92/M9ctj0PWYr101Gpe+dRhHG8Q22t9S2o7iZitGxfsncMHf3B4vnlorHiCRm6DDx7eORVyofwPDlAoZnvtpDhRyYOkesfC/ZzdUY9G4WCglBOcNp2s4EQ0eJpsbd3xUCLtLLNji3jNTJYebnuyWmUn4eH8TDtX7fr7687pKKBUy3D7b/2EaG4v1uG/5qeEv3ZmSFobrp0oLDAGASh9hncelRKp91jjdXnx5qAVfHm7FjnJDr98zl8cLm9MDvcWFSr0NO7rk6EZplfhJXgwuHR+HuTmR/Qodov7bWmrAewLhwnefmYLp6YMzyMXrBVoF3hsA4oGqgSYaMGt3eWCyuxEeMriDO4iIiCg4Nha1CtX9dGJiQAIBj9Oo5Hjrpom4+t/7sLPc9zzpi/wGyaGAte02bCkWu78A8MKVY/0W6trV9TNSsK/agA/21AnVf7K3vl/BrsLz93L9Cf929dRk/HHRqD6v29VvLhyBLw82oqXD4bO2ss2KyrYTw+hUCjnevmmSXwJNNSo5/vzTMbjqzX1C9d8L/DyKkvJe1dnN/H3ZbZP98hh0PeZr143HT1/7AUfrO4Rus6W4FcVNZoxKGJyzU7fHi6e+LhGuz00Mwye3T0FcmO8ZhhQqhQzPX54LpUKGd3fWCN3mmbWlWDQhQdr8fRhdw4lIzNbiNizd5fu6c/fcDEzPjAxCR/1jtAkGu2q636awSm/Fxz/U45ujzSho8P1c12F3o8PuRpPJcUoIfKRWiQvz4rFofALOGR0LlcK/Q2ivF2gVeK0EAAl+ft7qK9E+OueRLoSHcDtJIiIiIiIiIiIiIiIiIiIiIiJ/6N9fRBMRERERERERERERERERERERERERERHRaWtPlVho5Z1zArch8XFKuQx/XzwCoWrfATjrCvU+a0628nArvF7fdQCwMC8GC8b0fTN3X357fqbQ/QSAtYVtcHsEG++GrA/JW2MSdHjukpw+r3kynVqOW2Ym9fn2j8zPwOxM/22ofkZ2JCaliAWklrZY0WF3+2VdhYQNw0+2ZEEmpgUgmEunluPFy0cKb2bu9QLvCwSJDZRP85tR1Gz1XQggLlSFpTfm+j3Utas/X5SNM7LFNm6u1NuwPL9Z0vGH0zWciAYHk82Nm5cVCF9rR8dr8bNZfX8NcJxcBryweKTQNcbrBf7wdQV+8Vkx2ixiG9P74nB58PzGatz6gVigbXiIAn+9dESfAlBPDrjvSWpkSI9fszg8eHlbLWa+sA8Pfl6CjcV64SDe7rRbXfhoXxOuW3oU81/Jx8f7m/r1+pT6zmRz46EvSny+rxmToMPD56YHp6k+0FtdcAn+DMUPliCFcPE+mjvEQmuJiIho6NtTKRZaeceZgX/tppTL8MKVY4XeV6091iL5+F8ebBSfv4+Lx4K8OMlriHr8J2LvHwFgbUFLv97fyPsyf08MxXOLc/u85sl0agVumZ3W59s/uiAHs7Oj/NbPmTnRmJQmNs8vbbb4b/7elzfhP/rdwpGYluH/AD6dWoGXrh4naf7+3u5av/fhL5/uq0dRk9l3IYC4MDXeu3WS30Ndu/rzJaNxZo7YZ3mVbVYs31cv6fjD6RpORL4ZbS7832dHfc/lEkPxyAX++5w9kIw2sRnWycGuO8vbceM7+Zjz/Hd4YWO5UKirLwarC5/sq8ctSw9g1nM78PLmChis/pnvA4De4jzt5pHxUuaRJrHQWiIiIiIiIiIiIiIiIiIiIiIi8k3pu4SIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhhq9xSUUdhUfpsLUNP8HSnYnM1qDxRPjfAZH1hvtaLe6EKUV/5X4jcViGzADwM9nBzYEMVqnxFWT4/HO7gaftXqLC0caLJiYEtqntfqSJfq3S0dArZT3ab2eXDYhDn/bVC35drmJOtx9Zopfeznez4E635vMerxAUbMlaOdAd6anh/slFK8neYk63DwjEf/Z5fvnEQBWH23FH3+S1aewukDyeoGXt4tvev/C4hG9huL5g0Iuwz8Wj8C5Lx+A2eE7oOBfO+pw7dQEoWMPt2t4sJ07Mgq1T8wZ6DaIBpVjTRbc+2kxCpssQvVqpRwvXzkKSoV/njDyEnV4/ZrRuHXZMaEN2D8/2IKNxe24c04ybpye2Kcgb7PDjc8PtuCV7bWo0tuFbqNUyPDmtWOQm6CTvB4A1BrENmCPD+v+/mwsbsdvvipDTbtYv1IVNVvx0BelePP7ejy1KBuzMsXCesg/lqwpR72x958RpUKGf14+0u+v5/2pzSIefBqhEQvkCrSwEPE+9BYnEKsJYDdERER0OtBbnGgz+37dEx+mDkigZHcyY7S4fEoS3tvV+wyt3mBHu8WJKJ34+6hNha3CtT8/I7AhiNE6Fa6eloy3v6/xWau3OHGkvgMTU/s2P+vLjPRvV4z1++v1xZMS8bf1ZZJvl5cUhrvnZvi1FwC4bFIiDtQYfdZ5vF4UNnYE7RzozvTMSPwsgD+TeUlhuHl2Gv7zndjnI6sPN+GJRaMH5fz9pS2VwvUvXDkWqVGBfV+kkMvwj6vG4pwXdgrN31/dWoVrp4t93jTcruHBdu7oWNQ9fd5At0EkyZKVhag39D5zVSlkePHqcYN6LteVaHBqpLbzelLQ0IHfryrCjjJ9INtCg9GOv3xbile2VuJX5+XgltlpUPXzc4Y2i3jwacQg+fwyXCPeh17CvJWIiIiIiIiIiIiIiIiIiIiIiHo3OH6DiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiCqtbHxqPHBTr48GSXT4zDysOtSI8KQdrx/4sMQWqU+r//HSsxmMvl9uKHapNQbbROGZSAqksnxAkFuwJAfm1Hn4NdpVo0NhZT0sL8ftycWA1SItWoEwwrO+6x8zL6FE7ry9wR4httV7TZBzTYdcmCzICvcd9ZqXjvh0Y43b6D8uqNDuyvNQ3oY9KdrWXtKG+1CdUunhiH+aOiA9xRp9TIENx1RjL+vtl3kERJixX7asQe2+F0DSeigdVhd+OV7bV4/bt62F0eodvIZMDfLx2BcUn+ff1y7sgo/O2yEfjVl6VwCTxnGawuPL+xGi9srsGcrAjMzorAhORQpEWFIDFcDa1KDpVCDqfbA6vTg+YOJ2oNdhxtsGBvtQlbStthdYrdZ+DHQM3FIzE3p++BHq0CoSEAEHnSpu4utxe/XV2OZXt7D9f2l4JGC654+wjuOysVD89PhzIQL9joBN8ca8PyA80+6345Lw0TkoPz3qGvjDax0AhAWqBqIIWpxfsw2HwHChEREdHQV9suNqdKiw5uIPzlk5Pw5YFGpEdrkRatQVqU5n//++N/x4aqJR3T6fZiT5VBqDZap8Ls7Ki+tC7JpRMThYJdAWB/taHPwa5SLRqfgKnp/v/8ISdOh5RIDeoMYj93xz22YATkAUgQnTcyRri2ss06oMGuv1s4MuBr/OLsTLy3qxZOt+8ZQ73Bjn3VhgF9TLqztaQN5S0WodrFk5Nw3pjYAHfUKTVKg7vnZuBvG8p91pY0m7G3SuyxHU7XcCLy7ZujzVi+3/fn+g+em40JKYPr89PeiM7otCo5nvqmBK9tq4Lb43su7y8Gqwu//6oIH/5Qh1evHY8xiX2feYqG2AKn6TxSwv0jIiIiIiIiIiIiIiIiIiIiIqLeMdiViIiIiIiIiIiIiIiIiIiIiIiIiIiIaBgy2cXCZk4OjQq0WZkRKPjNDL8es6jZAodgENmM9PCABImebGpqGLQquVBY2NFGc+Ab+tFdZyQH7NhTUsNRZ2gVrs+K0eD80YEJ3xyToBN+/Kv10jbD96cZGeGYkRH4DYCTItS4YHQ01hS0CdVvKTEMumDXpbvFguxUChkePS8jwN2c6M4zUvDG9/XoELjufry/WeixHU7XcCIaGEcbzPjsYAs+2NskKYRRLgOevjgHiyfGBaSvKyfFIz0qBHd9UoTmDrEQVJfHi21lBmwrEwsa6oukCDXeuHo0pqX37/lRbxF7rCO1/9vU3WR3446PCgN6/7rj9QIvb6vF3moT3r4+F+GDZMP7oajN4sKjK8t81k1MCcX981KD0FH/GCUEn0oJMAikULVcuFbKNZOIiIiGLpNd7DVBRLBnN1lROPaHs/16zOIms/j8PTMyIEGiJ5uSHgmtSgGr0/drz4KGjoD3c9zdcwM3l5ySHiEp2DUrVovzcwPz3n1MYqjw41/VZg1IDyJmZEZhRmbgg4aTIkJwQW4c1hxpEqrfUtw26IJd390pFpSsUsjx2IIRAe7mRHfOzcDr26uE5u+f7KsXemyH0zWciHrXZnbikc+P+aybmBqOB87NCng//iQaBrpsT11QA11PVtDQgYWv7MbTl+bimml9+10Gk4R5XVjI4NiWUSdh3s55JBERERERERERERERERERERGR/wyO3yAiIiIiIiIiIiIiIiIiIiIiIiIiIiIioqByCW6+2S64oedglpsYivyHp6PF7ERThwPNHU60dDjR1OHs/DeTA81mJ5o7nMhN1AWlJ6VChlHxWhys8x3aWttuD0JHnWGn/Q0j683IeK2k+mumJCBQe/zLZUBOrBZHGnw//q2CAWuBcMuMpKCtdcXkeOFg191VxgB3I43J7sb6Yr1Q7eKJcUiPCglwRycKD1HgiknxeHd3g8/atcfa8MzFOT5/9ofTNZyIAsPjBcx2Nzocbugtzv9n787D5CrL9I/ftfa+r9mTzp6QkBBCCBAWlVVA2RV0gBnEhVHAmZ+OOjo66Cg6OijKIoqIiCL7jkAChCUkIfvaSTrpfd+rl+paf3/EaICk663uOlXVqe/nunJxkX7qnKc6VW+deqr7vXWg06uqdq8qWwe0tqZXTb2+qI+Zk+bQHZfO0HlzCi3o+B+WTcnVS59fqP/3zH6tMlz/rXT27AL95OLpKsl2jfpYnQNmYbWHgkOGAiFd/eAubaz3jPrcI7WmuldXPrBDf/zsPBVm8iurVviPZ/ervX/4x0aa066fXzpTTrv1IVmjNeA3C/2SpKwkCQyOpo8Bn/n9AwAAx65AMHVmN3PKs7TlWyvU3uc7OGvv+9sfz5Da/vZ3h742tzw7Lj25HDbNLM3U1obI75Xqu83DUEdjdlmWpWGdM0uzoqq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