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260 changes: 245 additions & 15 deletions your-code/lab_imbalance.ipynb
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,20 @@
"metadata": {},
"outputs": [],
"source": [
"# Your code here"
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.preprocessing import RobustScaler, StandardScaler, PolynomialFeatures, MinMaxScaler\n",
"import seaborn as sns\n",
"\n",
"import pandas as pd\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score"
]
},
{
Expand All @@ -44,11 +57,153 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Your response here"
"data = pd.read_csv('PS_20174392719_1491204439457_log.csv').sample(100000)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(100000, 11)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"step int64\n",
"type object\n",
"amount float64\n",
"nameOrig object\n",
"oldbalanceOrg float64\n",
"newbalanceOrig float64\n",
"nameDest object\n",
"oldbalanceDest float64\n",
"newbalanceDest float64\n",
"isFraud int64\n",
"isFlaggedFraud int64\n",
"dtype: object"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<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>nameOrig</th>\n",
" <th>oldbalanceOrg</th>\n",
" <th>newbalanceOrig</th>\n",
" <th>nameDest</th>\n",
" <th>oldbalanceDest</th>\n",
" <th>newbalanceDest</th>\n",
" <th>isFraud</th>\n",
" <th>isFlaggedFraud</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4465129</th>\n",
" <td>324</td>\n",
" <td>CASH_OUT</td>\n",
" <td>668156.34</td>\n",
" <td>C1251348464</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>C497599109</td>\n",
" <td>17157234.83</td>\n",
" <td>17825391.17</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5870062</th>\n",
" <td>403</td>\n",
" <td>TRANSFER</td>\n",
" <td>2544832.46</td>\n",
" <td>C478732865</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>C1754164558</td>\n",
" <td>7158922.07</td>\n",
" <td>9703754.54</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5956605</th>\n",
" <td>405</td>\n",
" <td>CASH_OUT</td>\n",
" <td>209591.50</td>\n",
" <td>C1733037259</td>\n",
" <td>51908.0</td>\n",
" <td>0.0</td>\n",
" <td>C711944103</td>\n",
" <td>0.00</td>\n",
" <td>209591.50</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" step type amount nameOrig oldbalanceOrg \\\n",
"4465129 324 CASH_OUT 668156.34 C1251348464 0.0 \n",
"5870062 403 TRANSFER 2544832.46 C478732865 0.0 \n",
"5956605 405 CASH_OUT 209591.50 C1733037259 51908.0 \n",
"\n",
" newbalanceOrig nameDest oldbalanceDest newbalanceDest isFraud \\\n",
"4465129 0.0 C497599109 17157234.83 17825391.17 0 \n",
"5870062 0.0 C1754164558 7158922.07 9703754.54 0 \n",
"5956605 0.0 C711944103 0.00 209591.50 0 \n",
"\n",
" isFlaggedFraud \n",
"4465129 0 \n",
"5870062 0 \n",
"5956605 0 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(data.shape)\n",
"display(data.dtypes)\n",
"display(data.head(3))\n"
]
},
{
Expand All @@ -60,27 +215,77 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"# Your code here\n"
"y = fraud['isFraud']\n",
"X = fraud.drop(labels='isFraud', axis=1)\n",
"\n",
"\n",
"# divide train test\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2)\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run a logisitc regression classifier and evaluate its accuracy."
"### Run a logistic regression classifier and evaluate its accuracy."
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 38,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No samples remaining after dropping missing values.\n"
]
}
],
"source": [
"# Your code here"
"import pandas as pd\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"# Assuming your training data is stored in the X_train DataFrame\n",
"# and the target variable is stored in the y_train Series\n",
"\n",
"# Identify the column(s) with string values\n",
"string_columns = X_train.select_dtypes(include='object').columns\n",
"\n",
"# Convert the string columns to numeric values\n",
"for column in string_columns:\n",
" X_train[column] = pd.to_numeric(X_train[column], errors='coerce')\n",
"\n",
"# Drop rows with missing values\n",
"X_train.dropna(inplace=True)\n",
"y_train = y_train[X_train.index]\n",
"\n",
"# Check if there are enough samples remaining\n",
"if len(X_train) == 0:\n",
" print(\"No samples remaining after dropping missing values.\")\n",
"else:\n",
" # Split the data into training and testing sets\n",
" X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n",
"\n",
" # Fit the logistic regression model\n",
" lr = LogisticRegression()\n",
" lr.fit(X_train, y_train)\n",
"\n",
" # Make predictions on the test set\n",
" y_pred = lr.predict(X_test)\n",
"\n",
" # Calculate the accuracy of the model\n",
" accuracy = accuracy_score(y_test, y_pred)\n",
" print(f\"Logistic Regression Test Accuracy: {accuracy * 100:.2f}%\")\n",
"\n"
]
},
{
Expand All @@ -92,11 +297,36 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 40,
"metadata": {},
"outputs": [],
"outputs": [
{
"ename": "ValueError",
"evalue": "Found array with 0 sample(s) (shape=(0, 10)) while a minimum of 1 is required by DecisionTreeClassifier.",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[40], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mtree\u001b[39;00m \u001b[39mimport\u001b[39;00m DecisionTreeClassifier\n\u001b[0;32m 2\u001b[0m dtc \u001b[39m=\u001b[39m DecisionTreeClassifier()\n\u001b[1;32m----> 3\u001b[0m dtc\u001b[39m.\u001b[39;49mfit(X_train, y_train)\n\u001b[0;32m 4\u001b[0m y_pred \u001b[39m=\u001b[39m dtc\u001b[39m.\u001b[39mpredict(X_test)\n\u001b[0;32m 5\u001b[0m acc \u001b[39m=\u001b[39m dtc\u001b[39m.\u001b[39mscore(X_test, y_test)\u001b[39m*\u001b[39m\u001b[39m100\u001b[39m\n",
"File \u001b[1;32mc:\\Users\\jsctr\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:889\u001b[0m, in \u001b[0;36mDecisionTreeClassifier.fit\u001b[1;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[0;32m 859\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfit\u001b[39m(\u001b[39mself\u001b[39m, X, y, sample_weight\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, check_input\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m):\n\u001b[0;32m 860\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Build a decision tree classifier from the training set (X, y).\u001b[39;00m\n\u001b[0;32m 861\u001b[0m \n\u001b[0;32m 862\u001b[0m \u001b[39m Parameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 886\u001b[0m \u001b[39m Fitted estimator.\u001b[39;00m\n\u001b[0;32m 887\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 889\u001b[0m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mfit(\n\u001b[0;32m 890\u001b[0m X,\n\u001b[0;32m 891\u001b[0m y,\n\u001b[0;32m 892\u001b[0m sample_weight\u001b[39m=\u001b[39;49msample_weight,\n\u001b[0;32m 893\u001b[0m check_input\u001b[39m=\u001b[39;49mcheck_input,\n\u001b[0;32m 894\u001b[0m )\n\u001b[0;32m 895\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\n",
"File \u001b[1;32mc:\\Users\\jsctr\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:186\u001b[0m, in \u001b[0;36mBaseDecisionTree.fit\u001b[1;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[0;32m 184\u001b[0m check_X_params \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(dtype\u001b[39m=\u001b[39mDTYPE, accept_sparse\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcsc\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 185\u001b[0m check_y_params \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(ensure_2d\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, dtype\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m)\n\u001b[1;32m--> 186\u001b[0m X, y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_data(\n\u001b[0;32m 187\u001b[0m X, y, validate_separately\u001b[39m=\u001b[39;49m(check_X_params, check_y_params)\n\u001b[0;32m 188\u001b[0m )\n\u001b[0;32m 189\u001b[0m \u001b[39mif\u001b[39;00m issparse(X):\n\u001b[0;32m 190\u001b[0m X\u001b[39m.\u001b[39msort_indices()\n",
"File \u001b[1;32mc:\\Users\\jsctr\\anaconda3\\lib\\site-packages\\sklearn\\base.py:579\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m 577\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mestimator\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m check_X_params:\n\u001b[0;32m 578\u001b[0m check_X_params \u001b[39m=\u001b[39m {\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mdefault_check_params, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_X_params}\n\u001b[1;32m--> 579\u001b[0m X \u001b[39m=\u001b[39m check_array(X, input_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mX\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_X_params)\n\u001b[0;32m 580\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mestimator\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m check_y_params:\n\u001b[0;32m 581\u001b[0m check_y_params \u001b[39m=\u001b[39m {\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mdefault_check_params, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_y_params}\n",
"File \u001b[1;32mc:\\Users\\jsctr\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:931\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m 929\u001b[0m n_samples \u001b[39m=\u001b[39m _num_samples(array)\n\u001b[0;32m 930\u001b[0m \u001b[39mif\u001b[39;00m n_samples \u001b[39m<\u001b[39m ensure_min_samples:\n\u001b[1;32m--> 931\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 932\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mFound array with \u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m sample(s) (shape=\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m) while a\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 933\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m minimum of \u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m is required\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 934\u001b[0m \u001b[39m%\u001b[39m (n_samples, array\u001b[39m.\u001b[39mshape, ensure_min_samples, context)\n\u001b[0;32m 935\u001b[0m )\n\u001b[0;32m 937\u001b[0m \u001b[39mif\u001b[39;00m ensure_min_features \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m \u001b[39mand\u001b[39;00m array\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m2\u001b[39m:\n\u001b[0;32m 938\u001b[0m n_features \u001b[39m=\u001b[39m array\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m]\n",
"\u001b[1;31mValueError\u001b[0m: Found array with 0 sample(s) (shape=(0, 10)) while a minimum of 1 is required by DecisionTreeClassifier."
]
}
],
"source": [
"# Your code here"
"from sklearn.tree import DecisionTreeClassifier\n",
"dtc = DecisionTreeClassifier()\n",
"dtc.fit(X_train, y_train)\n",
"y_pred = dtc.predict(X_test)\n",
"acc = dtc.score(X_test, y_test)*100\n",
"print(f\"Decision Tree Test Accuracy {round(acc, 2)}%\")\n",
"cm = confusion_matrix(y_test, y_pred)\n",
"print(cm)\n",
"\n",
"print('Precision ', cm[1,1]/(cm[0,1] + cm[1,1]))"
]
},
{
Expand All @@ -108,11 +338,11 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Your response here"
"# I completely and utterly messed up and don't even know how to fix it even with chatGPT"
]
},
{
Expand All @@ -139,7 +369,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
"version": "3.10.9"
}
},
"nbformat": 4,
Expand Down