Upload tracingonlinedating_159.py

tracingonlinedating_159.py

@@ -0,0 +1,76 @@
+# -*- coding: utf-8 -*-
+"""Tracingonlinedating.159
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1OkkJMge8YJRdezVwRU92t1timr9gJw9M
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import warnings
+warnings.filterwarnings('ignore')
+
+df = pd.read_csv("/content/Online_Dating_Behavior_Dataset.csv")
+
+print(df.head())
+
+print(df.describe())
+
+print(df.isnull().sum())
+
+plt.figure(figsize=(10, 6))
+sns.histplot(df['Matches'], bins=30, kde=True)
+plt.title('Distribution of Matches')
+plt.xlabel('Number of Matches')
+plt.ylabel('Frequency')
+plt.show()
+
+sns.pairplot(df)
+plt.show()
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+
+scaler = StandardScaler()
+numerical_features = ['Income', 'Age', 'Attractiveness', 'Children']
+df[numerical_features] = scaler.fit_transform(df[numerical_features])
+
+X = df.drop('Matches', axis=1)
+y = df['Matches']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+print("Training set shape:", X_train.shape)
+print("Testing set shape:", X_test.shape)
+
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error, r2_score
+
+lr_model = LinearRegression()
+rf_model = RandomForestRegressor(random_state=42)
+
+lr_model.fit(X_train, y_train)
+y_pred_lr = lr_model.predict(X_test)
+print("Linear Regression - RMSE:", mean_squared_error(y_test, y_pred_lr, squared=False))
+print("Linear Regression - R^2 Score:", r2_score(y_test, y_pred_lr))
+
+
+rf_model.fit(X_train, y_train)
+y_pred_rf = rf_model.predict(X_test)
+print("Random Forest - RMSE:", mean_squared_error(y_test, y_pred_rf, squared=False))
+print("Random Forest - R^2 Score:", r2_score(y_test, y_pred_rf))
+
+importance = rf_model.feature_importances_
+features = X.columns
+indices = np.argsort(importance)[::-1]
+
+plt.figure(figsize=(12, 6))
+plt.title("Feature Importances")
+plt.bar(range(X.shape[1]), importance[indices], align="center")
+plt.xticks(range(X.shape[1]), features[indices], rotation=90)
+plt.xlim([-1, X.shape[1]])
+plt.show()
+