The objective of this project is to identify the type of black hole using a dataset provided for the "BlackHole Classification" competition on Kaggle. The analysis involves data preprocessing, exploratory data analysis, feature engineering, and model building where I tried different models and approaches and recorded them to predict the target variable related to the conditions of the black hole.

The dataset contains various features, including luminosity and radio freq information. Key columns include:

• Multiple numerical and categorical features representing data.

1. Loading Data: The data is loaded from CSV files.
2. Handling Missing Values: Missing values are addressed by imputation or removal.In our case we used KNN Imputing
3. Encoding Categorical Variables: Categorical variables are encoded using techniques like one-hot encoding.

• Visualization: Histograms, scatter plots, and correlation matrices are used to understand data distribution and relationships.
• Summary Statistics: Descriptive statistics provide insights into the central tendency and variability of features.

• Outliers- these were casted out using methods like DBSCAN And Kneepointing

1. Model Selection: Various models, including Logistic Regression, Decision Trees, and Random Forest, are evaluated.
2. Hyperparameter Tuning: Grid search and cross-validation are used to optimize model parameters.
3. Evaluation Metrics: Models are evaluated using metrics such as accuracy, precision, recall, and F1-score.
4. Model Testing: Models like XGBOOST , CARTBOOSTING and VOTING classifiers were introduced.

• Model Performance: The performance of different models is compared, and the best-performing model is selected.
• Feature Importance: Key features contributing to the model's predictions are identified.

The project successfully identifies the most significant features and builds a predictive model for classifying black holes.