📘 General Steps to Run Programs in Anaconda / Jupyter Notebook

1. Download and install Anaconda if not installed.
2. Open Anaconda Navigator.
3. Click Launch under Jupyter Notebook.
4. Jupyter will open in your browser at http://localhost:8888/tree.
5. Create a new notebook: Click New → Python 3.
6. Write your Python code in a cell and press Shift + Enter to execute.
7. Save your notebook regularly using File → Save and Checkpoint.

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

1. Identify problems that require loops (for, while) or conditional statements (if-else).
2. Define functions for repeated tasks.
3. Import a dataset using pandas from a CSV or Excel file.
4. Analyze the data: read, filter, sort, or summarize.
5. Export your processed data back to CSV or Excel for future use.
6. Verify the exported file in the working directory to ensure it is correct.

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🤖 Problem Set 2 – KNN Classification

1. Import the necessary libraries: pandas, scikit-learn.
2. Load a suitable dataset for classification (e.g., Iris Dataset).
3. Explore the dataset to understand features and labels.
4. Split the dataset into training and testing sets.
5. Create a KNN classifier object and train it with the training set.
6. Test the model using the testing set and record predictions.
7. Evaluate model performance using accuracy, confusion matrix, or classification report.

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🧠 Problem Set 3 – Naive Bayes Classification

1. Import necessary libraries like pandas and scikit-learn.
2. Load a dataset suitable for classification (e.g., Iris, Titanic dataset).
3. Explore and preprocess the data if required (handle missing values, encode categorical variables).
4. Split the dataset into training and testing sets.
5. Train a Naive Bayes classifier on the training set.
6. Use the model to predict on the testing set.
7. Evaluate model performance using metrics like accuracy, precision, recall, and F1-score.

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📊 Problem Set 4 – K-Means Clustering

1. Import libraries like pandas and scikit-learn for clustering.
2. Load a dataset suitable for clustering (e.g., Iris or customer dataset).
3. Explore the dataset and select relevant features for clustering.
4. Decide the number of clusters (k) based on your problem.
5. Apply K-Means algorithm to the data.
6. Check the cluster centers and assigned labels for each data point.
7. Optionally, visualize the clusters using scatter plots for better understanding.
8. Export the clustered dataset to a CSV file for reference.

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1. Open Anaconda Navigator → Launch Jupyter Notebook.
2. Create a new Python 3 notebook.
3. Use loops, if-else, and functions to solve problems.
4. Import dataset (CSV/Excel) using pandas.
5. Analyze data (read, filter, summarize).
6. Export the processed data to CSV/Excel.
7. Verify the exported file in your folder.

1. Open Jupyter Notebook.
2. Import required libraries (pandas, scikit-learn).
3. Load a dataset (e.g., Iris dataset).
4. Split data into training and testing sets.
5. Create and train KNN model.
6. Test the model and check accuracy.

1. Open Jupyter Notebook.
2. Import required libraries.
3. Load a dataset suitable for classification.
4. Split data into training and testing sets.
5. Train Naive Bayes model and test it.
6. Evaluate accuracy and other metrics.

1. Open Jupyter Notebook.
2. Import libraries for clustering (pandas, scikit-learn).
3. Load dataset for clustering.
4. Decide number of clusters (k) and apply K-Means.
5. Check cluster centers and labels.
6. Export clustered data to CSV if needed.

CSV Data Preview:

        Name,Age,Marks
        Amit,20,78
        Riya,21,85
        Karan,19,90
        Sneha,22,67
        Rohit,23,80
        Anita,21,75
        Neha,24,88
        Vikas,25,92
                        

Copy CSV Data Download Students CSV

CSV Data Preview:

        SepalLength,SepalWidth,PetalLength,PetalWidth,Species
        5.1,3.5,1.4,0.2,setosa
        4.9,3.0,1.4,0.2,setosa
        5.8,2.7,5.1,1.9,virginica
        6.2,3.4,5.4,2.3,virginica
        5.7,2.8,4.1,1.3,versicolor
        6.1,2.6,5.6,1.4,virginica
        5.5,2.4,3.8,1.1,versicolor
                        

Copy CSV Data Download Iris CSV

CSV Data Preview:

        Feature1,Feature2
        5.1,3.5
        4.9,3.0
        6.2,3.4
        5.9,3.0
        5.7,2.8
        6.1,2.6
        5.5,2.4
        6.3,3.3
                        

Copy CSV Data Download Cluster Data CSV