The DSLR (Data Science and Logistic Regression) project from 42 School is centered around implementing logistic regression to classify and analyze data from a given dataset. This project aims to provide students with a practical understanding of machine learning principles, particularly logistic regression, data preprocessing, and model evaluation.
- Data Preprocessing: Reads and cleans data to prepare it for analysis and model training.
- Logistic Regression Implementation: Implements logistic regression from scratch to classify data points effectively.
- Model Training and Testing: Trains the logistic regression model using a dataset and evaluates its performance using metrics such as accuracy, precision, and recall.
- Data Visualization: Visualizes data distribution and model predictions to gain insights into the classification process.
This script plots a histogram for the feature with a homogeneous score.
This script generates a scatter plot to visualize the relationship between two features.
This script creates a pair plot to show pairwise relationships between features.
This script runs a custom describe function to summarize statistics for each feature.
histogram.py: Plots histogram for the feature with homogeneous score. See the result in the image above.scatterplot.py: Generates scatter plot for feature comparisons.pairplot.py: Creates pair plots for visual analysis.describe.py: Outputs statistical summary of features.
git clone https://github.com/usmanUA/DSLR-LogisticRegression.git
cd DSLR-LogisticRegression
python3 main.py datasets/dataset_train.csv action=[histogram, scatterplot, pairplot, train]
python3 main.py datasets/dataset_test.csv action=[predict]