Data Science, Data Analysis & Machine Learning using Python [Internship Assured]

1. Student Grade Calculator

Objective:

Create a Python script that:

• Accepts student name and marks in 3 subjects via user input
• Calculates total and average marks
• Prints result with formatted output and remarks ("Pass"/"Fail" based on average)

Concepts Used:

Variables, Input/Output, Typecasting, Conditionals, Arithmetic Operators, Functions

2. Employee Payroll System

Objective:

Build a program that:

• Uses a function to compute salary (basic + allowances – deductions)
• Reads and writes employee payroll info to a text file
• Handles exceptions for invalid input types

Concepts Used:

Functions, File I/O, Try-Except, Arithmetic Operations

3. Python Data Toolkit

Objective:

Write a program to:

• Store and manage employee data using Dictionary and List
• Display filtered results using conditional logic (e.g., salary > threshold)
• Add options to update or delete a record

Concepts Used:

Data Types, Loops, Conditionals, Functions, Data Structures

1. Bank Account Class Simulator

Objective:

Design a Python class BankAccount that allows users to:

• Create an account with name and balance
• Deposit and withdraw money
• Display account summary

Concepts Used:

Object-Oriented Programming (Classes, Methods, Constructor)

2. NumPy Data Stats Tool

Objective:

Use NumPy to:

• Create a 2D array of random student scores
• Perform basic operations (mean, max, min, std)
• Slice rows/columns to filter scores

Concepts Used:

NumPy Arrays, Indexing, Math Operations

3. Pandas Data Cleaner & Visualizer

Objective:

• Import a CSV dataset using Pandas
• Clean data: handle missing values, drop/rename columns
• Visualize trends using Matplotlib and Seaborn (bar plot, histogram)

Concepts Used:

Pandas (DataFrames), Data Wrangling, Matplotlib, Seaborn

1. Data Quality Analyzer

Objective:

Load a real-world dataset and:

• Identify and treat missing values
• Detect outliers using Z-score & IQR
• Check for skewness and apply transformations

Concepts Used:

Missing Values, Outliers, Skewness Handling

2. Statistical Summary & Hypothesis Testing

Objective:

• Generate descriptive stats (mean, median, std, variance)
• Visualize distributions using KDE plots
• Perform t-test and ANOVA to validate assumptions

Concepts Used:

Descriptive & Inferential Statistics, Hypothesis Testing

3. Exploratory Data Analysis Report

Objective:

• Perform univariate and bivariate analysis
• Create visual summaries (heatmaps, barplots, violin plots)
• Generate an auto EDA report using pandas-profiling or Sweetviz

Concepts Used:

EDA Workflow, Visualization, Automated EDA Tools

1. Exploratory Data Dive

Objective:

Use a public dataset to:

• Inspect structure using .info(), .shape, .dtypes
• Perform univariate and multivariate analysis
• Visualize distributions using histograms, boxplots, and pairplots

Concepts Used:

Dataset Understanding, Univariate & Multivariate Analysis

2. Clean & Transform

Objective:

• Handle missing data with imputation techniques
• Detect and treat outliers using Z-score and IQR
• Fix skewed features using log and square-root transformations

Concepts Used:

Missing Values, Outlier Treatment, Skewness Correction

3. EDA Notebook & Reporting

Objective:

• Perform end-to-end EDA
• Add markdown explanations and visualizations
• Generate EDA reports using pandas-profiling or sweetviz

Concepts Used:

EDA Workflow, Reporting, Automated EDA Tools

1. Predict House Prices – Regression Models

Objective:

Build and compare:

• Simple & Multiple Linear Regression
• Ridge & Lasso Regularization
• Evaluate performance using RMSE, R²

Concepts Used:

Linear & Non-linear Regression, Regularization

2. Classify Customer Churn – Binary Classification

Objective:

• Apply Logistic Regression, KNN, and SVM
• Use metrics like Accuracy, Precision, Recall, F1
• Visualize model performance with Confusion Matrix & ROC curve

Concepts Used:

Classification Models, Performance Evaluation

3. Optimize Models – CV & PCA

Objective:

• Implement K-Fold Cross Validation
• Tune hyperparameters using GridSearchCV
• Apply PCA to reduce features and improve model efficiency

Concepts Used:

Cross Validation, Hyperparameter Tuning, Dimensionality Reduction

1. Predict Employee Attrition – Ensemble Models

Objective:

• Train and compare Random Forest, Bagging, Boosting (XGBoost), and Stacking
• Analyze feature importance and model accuracy

Concepts Used:

Ensemble Learning, Model Comparison

2. Segment Customers – Clustering & Association Rules

Objective:

• Apply K-Means and Hierarchical Clustering for customer segmentation
• Use Apriori algorithm to uncover product association rules

Concepts Used:

Unsupervised Learning, Market Basket Analysis

3. Deploy a ML Model – Flask, Streamlit & Gradio

Objective:

Build a REST API using Flask for a trained model

• Create a Streamlit dashboard and Gradio interface
• Present a working ML solution as a capstone project

Concepts Used:

Model Deployment, Web App Interface, API Development