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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Class Distribution:\n",
+ " 0 3000\n",
+ "2 1500\n",
+ "1 1500\n",
+ "Name: 128, dtype: int64\n",
+ "Cross-validation scores: [0.96501458 0.94606414 0.95626822 0.9548105 0.95043732 0.95620438\n",
+ " 0.94744526]\n",
+ "Mean cross-validation score: 0.9537491981747265\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.99 0.99 0.99 602\n",
+ " 1 0.91 0.91 0.91 277\n",
+ " 2 0.93 0.93 0.93 321\n",
+ "\n",
+ " accuracy 0.96 1200\n",
+ " macro avg 0.94 0.94 0.94 1200\n",
+ "weighted avg 0.96 0.96 0.96 1200\n",
+ "\n",
+ "Training error: 0.0%\n",
+ "Validation error: 4.416666666666669%\n",
+ "\n",
+ "Error Analysis:\n",
+ "Number of errors in validation set: 53\n",
+ "Indices of validation errors: Int64Index([2338, 228, 506, 1580, 3185, 1477, 1027, 2344, 4096, 2417, 3066,\n",
+ " 5454, 3049, 706, 1498, 2410, 4095, 2899, 2373, 2127, 4920, 2244,\n",
+ " 3268, 303, 2209, 1221, 1513, 1609, 453, 1817, 1918, 1543, 4441,\n",
+ " 1095, 1978, 4473, 1002, 2836, 2197, 293, 2370, 471, 227, 3184,\n",
+ " 3838, 4440, 2364, 3839, 4998, 2432, 2874, 452, 3970],\n",
+ " dtype='int64')\n",
+ "[2 2 2 2 2 2 1 1 2 2 2 1 1 1 1 1 2 1 1 1 1 1 2 2 2 2 2 2 2 2 0 2 0 1 1 1 1\n",
+ " 1 2 1 1 1 1 2 2 2 2 2 2 1 2 2 2 1 2 2 2 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0\n",
+ " 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "from sklearn.model_selection import train_test_split, cross_val_score\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
+ "\n",
+ "# Load the training data using pandas\n",
+ "data = pd.read_csv('TrainingDataMulti.csv', header=None)\n",
+ "\n",
+ "# Separate the features from the labels\n",
+ "X = data.iloc[:, :-1] # all rows, all columns except the last\n",
+ "y = data.iloc[:, -1] # all rows, last column\n",
+ "\n",
+ "# Check the class distribution\n",
+ "print(\"Class Distribution:\\n\", y.value_counts())\n",
+ "\n",
+ "# Split the data into training and validation sets\n",
+ "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+ "\n",
+ "# Create a model and fit it to the training data\n",
+ "model = RandomForestClassifier(n_estimators=100, random_state=10)\n",
+ "model.fit(X_train, y_train)\n",
+ "\n",
+ "# Perform k-fold cross-validation on the training set\n",
+ "cv_scores = cross_val_score(model, X_train, y_train, cv=7) # 5-fold cross-validation\n",
+ "print(\"Cross-validation scores:\", cv_scores)\n",
+ "print(\"Mean cross-validation score:\", cv_scores.mean())\n",
+ "\n",
+ "# Evaluate the model\n",
+ "train_preds = model.predict(X_train)\n",
+ "val_preds = model.predict(X_val)\n",
+ "\n",
+ "train_error = 1 - accuracy_score(y_train, train_preds)\n",
+ "val_error = 1 - accuracy_score(y_val, val_preds)\n",
+ "\n",
+ "print(classification_report(y_val, val_preds))\n",
+ "\n",
+ "print(f\"Training error: {train_error * 100}%\")\n",
+ "print(f\"Validation error: {val_error * 100}%\")\n",
+ "\n",
+ "# Perform error analysis on validation data\n",
+ "errors = X_val[y_val != val_preds]\n",
+ "print(f\"\\nError Analysis:\\nNumber of errors in validation set: {len(errors)}\")\n",
+ "print(\"Indices of validation errors:\", errors.index)\n",
+ "\n",
+ "# Load the testing data\n",
+ "test_data = pd.read_csv('TestingDataMulti.csv', header=None)\n",
+ "\n",
+ "# Predict the labels for the testing data\n",
+ "test_preds = model.predict(test_data)\n",
+ "\n",
+ "# Print the predicted labels for clarity\n",
+ "print(test_preds)\n",
+ "\n",
+ "# Save the testing data with predicted labels as per specification\n",
+ "test_data['Predicted Label'] = test_preds\n",
+ "test_data.to_csv('TestingResultsMulti.csv', index=False, header=False)\n"
+ ]
+ }
+ ],
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