Major Projects :

Bernoulli Naive Bayes algorithm is used here to predict outcomes based on weather and car conditions.

🧠Project Objective: Predict the outcome (e.g., whether to go out or not) based on two factors:

Weather: Rainy or Sunny Car Condition: Broken or Working

pandas is used for data manipulation. BernoulliNB is the Naive Bayes classifier suitable for binary/boolean features.

Project on Random Forest Classifier used here predicts whether a person will default on a loan based on their gender and occupation.

🧠Project Objective: Predict if a person will default on a loan based on their gender and occupation using a Random Forest classification model.

📦 Step-by-Step Explanation: Import Required Libraries import pandas as pd from sklearn.ensemble import RandomForestClassifier import matplotlib.pyplot as plt pandas: For reading and managing the data.

sklearn.ensemble.RandomForestClassifier: ML model used for classification. matplotlib.pyplot: (not used in code here, but can be for visuals like feature importance, confusion matrix, etc.)

K-Means clustering solution is used here for a dataset with two features (A and B). It: Groups the data into 3 clusters. Calculates the centroids of the clusters. Visualizes the clusters and their centroids. Predicts the cluster for new data points provided by the user.

Lung Cancer Predictor : To provide actionable recommendations for reducing lung cancer risk based on the analysis.

K-Neighbours : ​The K-Nearest Neighbors (KNN) algorithm is widely used in machine learning for both classification and regression tasks due to its simplicity and effectiveness. Import Necessary Libraries like: pandas is used for data manipulation. MinMaxScaler scales features to a specific range. KNeighborsClassifier is the KNN algorithm from scikit-learn.