Lab # 10 - 20SW084.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"view-in-github"},"source":["<a href=\"https://colab.research.google.com/github/Laiba-Akhtar/Data_Science_and_Analytics_20sw057_task/blob/main/Lab10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"]},{"cell_type":"markdown","source":["***Lab # 10 Tasks***\n","\n","Implement two of the classifiers (Decision tree and naïve bayes) and compare the performance of each classifier."],"metadata":{"id":"ZdLOPlpPNJEs"}},{"cell_type":"code","execution_count":2,"metadata":{"id":"omxfNZpZG52S","executionInfo":{"status":"ok","timestamp":1696096044292,"user_tz":-300,"elapsed":5,"user":{"displayName":"Junaid Aslam","userId":"00783424550127806115"}}},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import seaborn as sb\n","import matplotlib.pyplot as plt\n"]},{"cell_type":"code","source":["df=pd.read_csv('/content/PS_20174392719_1491204439457_log.csv')"],"metadata":{"id":"-uyx6-oYIW_P","executionInfo":{"status":"ok","timestamp":1696096047059,"user_tz":-300,"elapsed":887,"user":{"displayName":"Junaid Aslam","userId":"00783424550127806115"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":["The below column reference:\n","\n","step: represents a unit of time where 1 step equals 1 hour\n","type: type of online transaction\n","amount: the amount of the transaction\n","nameOrig: customer starting the transaction\n","oldbalanceOrg: balance before the transaction\n","newbalanceOrig: balance after the transaction\n","nameDest: recipient of the transaction\n","oldbalanceDest: initial balance of recipient before the transaction\n","newbalanceDest: the new balance of recipient after the transaction\n","isFraud: fraud transaction\n"],"metadata":{"id":"kwtIbzGeN8rj"}},{"cell_type":"code","source":["df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":226},"id":"IDvNQgKqKXs_","outputId":"a55a2315-e2ac-455d-a936-4782876cb608","executionInfo":{"status":"ok","timestamp":1696096048791,"user_tz":-300,"elapsed":9,"user":{"displayName":"Junaid Aslam","userId":"00783424550127806115"}}},"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n","0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n","1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n","2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n","3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n","4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n","\n","      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n","0  M1979787155             0.0             0.0      0.0             0.0  \n","1  M2044282225             0.0             0.0      0.0             0.0  \n","2   C553264065             0.0             0.0      1.0             0.0  \n","3    C38997010         21182.0             0.0      1.0             0.0  \n","4  M1230701703             0.0             0.0      0.0             0.0  "],"text/html":["\n","  <div id=\"df-656777de-bf7d-41f2-9d57-3eb8ac4ed7bd\" 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","      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\n","395813    18  CASH_OUT  159460.65   C764863652       31216.00            0.00   \n","395814    18   PAYMENT    5310.17  C1759198927      203596.00       198285.83   \n","395815    18  CASH_OUT  200024.34  C2138093782       60236.00            0.00   \n","395816    18   PAYMENT   12610.12  C1700380760       99249.61        86639.49   \n","395817    18  CASH_OUT  407474.21   C784415919       86639.49            0.00   \n","\n","           nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n","0       M1979787155            0.00            0.00      0.0             0.0  \n","1       M2044282225            0.00            0.00      0.0             0.0  \n","2        C553264065            0.00            0.00      1.0             0.0  \n","3         C38997010        21182.00            0.00      1.0             0.0  \n","4       M1230701703            0.00            0.00      0.0             0.0  \n","...             ...             ...             ...      ...        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'oldbalanceDest', 'newbalanceDest','type']]\n","\n","X = pd.get_dummies(data=X, columns=['type'], drop_first=True)\n","\n","# Define target variable\n","y = df['isFraud']\n","\n","# Split the data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n","\n","print(df.shape)\n","print(X_train.shape[0])\n","print(X_test.shape[0])\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Bp9zvCxBNFdn","outputId":"5527bad1-8f4e-464b-a4cc-ac4aa998f052","executionInfo":{"status":"ok","timestamp":1696096063949,"user_tz":-300,"elapsed":5,"user":{"displayName":"Junaid Aslam","userId":"00783424550127806115"}}},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["(395818, 11)\n","277072\n","118746\n"]}]},{"cell_type":"markdown","source":["NAIVE BAYES"],"metadata":{"id":"lVy2qV316OHj"}},{"cell_type":"code","source":["\n","naive_bayes = GaussianNB()\n","naive_bayes.fit(X_train, y_train)\n","\n","predictions = naive_bayes.predict(X_test)"],"metadata":{"id":"KI72FSsnTpMb","executionInfo":{"status":"ok","timestamp":1696096067500,"user_tz":-300,"elapsed":4,"user":{"displayName":"Junaid Aslam","userId":"00783424550127806115"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","Accuracy_score2=accuracy_score(y_test,predictions)\n","Precision_score2=precision_score(y_test,predictions)\n","Recall_score2=recall_score(y_test,predictions)\n","F1_score2=f1_score(y_test,predictions)"],"metadata":{"id":"gSeo4tJVYS26","executionInfo":{"status":"ok","timestamp":1696096072322,"user_tz":-300,"elapsed":761,"user":{"displayName":"Junaid Aslam","userId":"00783424550127806115"}}},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":["Decision Tree"],"metadata":{"id":"cEWEYb4y6SK_"}},{"cell_type":"code","source":["\n","model = DecisionTreeClassifier()\n","model.fit(X_train, y_train)\n","\n","# Predict with Decision Tree Classifier\n","tree_predictions = model.predict(X_test)"],"metadata":{"id":"w1eLE7ktpPl3","executionInfo":{"status":"ok","timestamp":1696096079017,"user_tz":-300,"elapsed":4852,"user":{"displayName":"Junaid Aslam","userId":"00783424550127806115"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","Accuracy_score1=accuracy_score(y_test, tree_predictions)\n","Precision_score1=precision_score(y_test, tree_predictions)\n","Recall_score1=recall_score(y_test, tree_predictions)\n","F1_score1=f1_score(y_test, tree_predictions)"],"metadata":{"id":"XRz4ixdw4r43","executionInfo":{"status":"ok","timestamp":1696096081466,"user_tz":-300,"elapsed":488,"user":{"displayName":"Junaid Aslam","userId":"00783424550127806115"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["# Making predictions\n","y_train_pred = model.predict(X_train)\n","y_test_pred = model.predict(X_test)\n","\n","# Calculate the accuracy\n","from sklearn.metrics import accuracy_score\n","train_accuracy = accuracy_score(y_train, y_train_pred)\n","test_accuracy = accuracy_score(y_test, y_test_pred)\n","print('The training accuracy is', train_accuracy)\n","print('The test accuracy is', test_accuracy)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SnyVll5O6vSL","outputId":"b7331587-2b22-4785-8692-ad3bb697ffbd","executionInfo":{"status":"ok","timestamp":1696096083333,"user_tz":-300,"elapsed":4,"user":{"displayName":"Junaid Aslam","userId":"00783424550127806115"}}},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["The training accuracy is 1.0\n","The test accuracy is 0.9995705118488202\n"]}]},{"cell_type":"markdown","source":["Comparision\n"],"metadata":{"id":"OCe8YyfK8iby"}},{"cell_type":"code","source":["import pandas as pd\n","\n","data = {\n","    'Metric': ['Accuracy', 'Precision', 'Recall', 'F1-Score'],\n","    'Decision Tree': [Accuracy_score1, Precision_score1, Recall_score1, F1_score1],\n","    'naïve bayes': [Accuracy_score2, Precision_score2, Recall_score2, F1_score2]\n","}\n","\n","df = pd.DataFrame(data)\n","print(df)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rSUEAlVv8kjG","outputId":"4828fd9c-1b5e-4508-894e-7e856b2e51e7","executionInfo":{"status":"ok","timestamp":1696096086239,"user_tz":-300,"elapsed":3,"user":{"displayName":"Junaid Aslam","userId":"00783424550127806115"}}},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["      Metric  Decision Tree  naïve bayes\n","0   Accuracy       0.999571     0.984252\n","1  Precision       0.456140     0.006494\n","2     Recall       0.565217     0.260870\n","3   F1-Score       0.504854     0.012672\n"]}]},{"cell_type":"code","source":["from scipy.stats import ttest_rel\n","\n","# Assuming you have arrays of evaluation scores for both classifiers (e.g., accuracy_scores1, accuracy_scores2)\n","t_stat, p_value = ttest_rel(Accuracy_score1,Accuracy_score2)\n","\n","if p_value < 0.05:\n","    print(\"There is a significant difference in performance.\")\n","else:\n","    print(\"There is no significant difference in performance.\");\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"W1aXoA54-gmz","outputId":"497c4667-37fd-4c93-b769-7ca4753f3351","executionInfo":{"status":"ok","timestamp":1696096089772,"user_tz":-300,"elapsed":632,"user":{"displayName":"Junaid Aslam","userId":"00783424550127806115"}}},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["There is no significant difference in performance.\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/scipy/stats/_stats_py.py:1103: RuntimeWarning: divide by zero encountered in divide\n","  var *= np.divide(n, n-ddof)  # to avoid error on division by zero\n","/usr/local/lib/python3.10/dist-packages/scipy/stats/_stats_py.py:1103: RuntimeWarning: invalid value encountered in double_scalars\n","  var *= np.divide(n, n-ddof)  # to avoid error on division by zero\n"]}]}]}