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