In this task I build classification models to predict whether the cancer type is Malignant or Benign. Also fine-tune the hyperparameters & compare the evaluation metrics of various classification algorithms.

• Karan Manoharan

1. Data Loading
2. Exploratory Data Analysis
3. Model Fitting
4. Conclusion

The data was given by "Info Pillar Solution from Kaggle".

Data include following columns:

• id
• diagnosis
• radius_mean
• texture_mean
• perimeter_mean
• area_mean
• smoothness_mean
• compactness_mean
• concavity_mean
• concave points_mean
• symmetry_mean
• fractal_dimension_mean
• radius_se
• texture_se
• perimeter_se
• area_se
• smoothness_se
• compactness_se
• concavity_se
• concave points_se
• symmetry_se
• fractal_dimension_se
• radius_worst
• texture_worst
• perimeter_worst
• area_worst
• smoothness_worst
• compactness_worst
• concavity_worst
• concave points_worst
• symmetry_worst
• fractal_dimension_worst

In this EDA part I visualized many plots using seaborn, matplotlib libraries

Logistic Regression model was trained and I got accuracy 0.96%

Random Forest Regression model was trained and I got accuracy 0.98

Support Vector Machine model was trained and I got accuracy 0.96

Decision Tree Classification model was trained and I got accuracy 0.92

In this task, I predicted the cancer type is Malignant or Benign.