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applying Linear Regression (Simple and Multiple), Polynomial Regression, and Logistic Regression on real-world datasets. analyze, build, evaluate, and compare models using scikit-learn in Python.
m10-as/Machine-Learning
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applying Linear Regression (Simple and Multiple), Polynomial Regression, and Logistic Regression on real-world datasets. You will analyze, build, evaluate, and compare models using scikit-learn. Regression Models: Choose a regression dataset (e.g.,student performance). 1.Exploratory Data Analysis (EDA): •Loaded the data and clean it (handle missing values, encoding, extra if any). •Visualize the data using appropriate plots (scatter plots, histograms, correlation heatmap). 2.Preprocessing: •Select appropriate numeric features for regression. •Scale the features. •Split the data into training and testing sets (e.g., 80/20). 3.Simple Linear Regression: •Picked one feature that most correlates with the target variable. •Fit a simple linear regression model. •Visualize the regression line and report metrics (R², MSE). 4.Multiple Linear Regression: •Use multiple features to predict the target. 5.Polynomial Regression: •Apply Polynomial Regression on the best-performing simple linear feature. •Try polynomial degrees 2, 3, and 4. •Scale the polynomial features if needed. •Compare models using visualizations and metrics. Logistic Regression with Classification: Choose a classification dataset (e.g., diabetes prediction) 1.Preprocessing: •Encode categorical variables and clean the data. •Scale features if needed. •Split the dataset into training and testing sets (e.g., 80/20 split). 2.Modeling with Logistic Regression: •Train a logistic regression model and Predict on your data. 3.Model Evaluation: •Generate a confusion matrix. •Visualize the confusion matrix using seaborn or matplotlib.
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applying Linear Regression (Simple and Multiple), Polynomial Regression, and Logistic Regression on real-world datasets. analyze, build, evaluate, and compare models using scikit-learn in Python.