🔹 General Steps to Run Programs in Anaconda / Jupyter Notebook

1. Install Anaconda (from anaconda.com/download).
2. Open Anaconda Navigator.
3. Click Launch under Jupyter Notebook.
4. Your browser will open at http://localhost:8888/tree.
5. Click New → Python 3 (ipykernel) to create a new notebook.
6. Write or paste your Python code in a cell.
7. Press Shift + Enter to execute the cell.
8. Save your work: File → Save and Checkpoint.

🧩 Problem Set 1 – Control Structures & Data Import/Export

1. Use loops (for/while) and if-else statements for logic building.
2. Import data with import pandas as pd → pd.read_csv('filename.csv').
3. Clean and process data (drop missing values, rename columns, etc.).
4. Export using df.to_csv('output.csv').

🤖 Problem Set 2 – KNN Classification (e.g., Iris Dataset)

1. Import: from sklearn.neighbors import KNeighborsClassifier.
2. Load data: from sklearn.datasets import load_iris.
3. Split dataset using train_test_split.
4. Fit model: knn.fit(X_train, y_train).
5. Predict: y_pred = knn.predict(X_test).
6. Evaluate accuracy using accuracy_score().

🧠 Problem Set 3 – Naïve Bayes Classifier

1. Import: from sklearn.naive_bayes import GaussianNB.
2. Prepare features and labels (X, y).
3. Train the model: model.fit(X_train, y_train).
4. Predict using model.predict(X_test).
5. Evaluate using accuracy, precision, recall, and F1-score.

🛒 Problem Set 4 – Apriori Algorithm (Market Basket Analysis)

1. Install library: pip install mlxtend.
2. Import: from mlxtend.frequent_patterns import apriori, association_rules.
3. Load dataset (e.g., Online Retail Dataset).
4. Convert transactions into 0/1 format using one-hot encoding.
5. Apply apriori(df, min_support=0.05, use_colnames=True).
6. Generate rules using association_rules().

📊 Problem Set 5 – K-Means Clustering

1. Import: from sklearn.cluster import KMeans.
2. Load a dataset (e.g., Iris, Customer, or Fruit dataset).
3. Preprocess the data (scaling if needed).
4. Choose number of clusters (k) using the elbow method.
5. Apply clustering: kmeans = KMeans(n_clusters=k).
6. Visualize using matplotlib.pyplot scatter plots.

📈 Problem Set 6 – Model Comparison and Evaluation

1. Use datasets like Iris, Fruits, or Online Retail.
2. Compare models: Logistic Regression, SVM, and KNN using accuracy scores.
3. Visualize results with bar or line charts.

pip install pandas numpy matplotlib scikit-learn mlxtend