This repository contains a collection of small projects and assignments completed during my early coding and machine learning practice. These exercises helped me understand fundamental programming concepts, data preprocessing, exploratory analysis, model development, and evaluation using Python.
The projects cover supervised machine learning methods such as linear regression, polynomial regression, logistic regression, and decision trees, along with a simple Python word-guessing game.
Files: DigitalAd_dataset.csv and sale_prediction_age_salary_prediction_logistic_regression
This project uses logistic regression to predict whether a customer is likely to purchase a product after viewing a digital advertisement.
The model uses features such as:
- Age
- Estimated salary
- Purchase status
Concepts practised:
- Loading and exploring datasets
- Data preprocessing
- Train-test splitting
- Feature scaling
- Logistic regression
- Classification prediction
- Model evaluation
Folder: breast_cancer_evaluation_of_models
This project explores the application of machine learning models to breast cancer classification data. The objective is to distinguish between diagnostic classes using clinical or tumour-related features.
Concepts practised:
- Data exploration and preprocessing
- Classification model development
- Comparison of machine learning algorithms
- Accuracy and performance evaluation
- Confusion matrix interpretation
- Understanding biomedical applications of machine learning
This assignment was particularly useful for connecting my interests in biomedical research with data science and predictive modelling.
Folder: guess_the_word
A simple Python-based word-guessing game created to practise core programming concepts.
Concepts practised:
- Variables and data types
- Conditional statements
- Loops
- User input
- String manipulation
- Basic game logic
- Error handling
Folder: height_prediction_From_age_using_decisiontree
This project applies a decision tree regression model to predict height based on age.
Concepts practised:
- Regression using decision trees
- Preparing input and target variables
- Training a machine learning model
- Generating predictions
- Visualizing decision boundaries and model behaviour
- Understanding non-linear relationships
Folder: house_price_prediction_using_linear_regression
This project uses linear regression to estimate house prices from one or more property-related variables.
Concepts practised:
- Linear regression
- Feature and target selection
- Data visualization
- Model fitting
- Price prediction
- Interpretation of regression relationships
- Evaluation of prediction performance
Folder: salary_prediction_LR_to_PR
This project compares linear regression and polynomial regression for predicting salary based on professional experience or position level.
Concepts practised:
- Linear regression
- Polynomial feature transformation
- Polynomial regression
- Comparison of linear and non-linear models
- Data visualization
- Prediction using fitted models
- Understanding underfitting and model complexity
- Python
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Scikit-learn
Through these assignments, I developed foundational experience in:
- Python programming
- Data cleaning and preprocessing
- Exploratory data analysis
- Regression and classification
- Model training and prediction
- Machine learning model evaluation
- Data visualization
- Applying computational methods to real-world datasets
These projects represent my early learning journey in Python and machine learning. Although they are small practice assignments, they helped establish the foundation for my later work in bioinformatics, genomics, transcriptomics, and biomedical data analysis.
The repository is maintained as a record of my progression from introductory coding exercises to more advanced computational biology and research-oriented analyses.
Future updates may include:
- Improved code documentation
- Additional model evaluation metrics
- Cross-validation
- Hyperparameter optimization
- More informative visualizations
- Better project-specific documentation
- Application of machine learning methods to biological datasets
Adya Shreya