Mar 2025 – May 2025 · 2 mos · Remote

• From March 10 to May 10, 2025, Saksham successfully completed a 2-month Machine Learning & Data Science Internship at Feynn Labs, in collaboration with Chandigarh University.
• During the internship, Saksham worked on real-world AI projects, combining data-driven development, business modeling, and market analysis.
• ⸻
• 📌 Key Projects & What They Involved:
• 1. AI Product/Service Prototyping
• Developed a basic working model of an AI-powered product or service using machine learning techniques, simulating real-life use cases like automation or predictive analysis.
• 2. Market Segmentation using Machine Learning
• Applied clustering algorithms to identify distinct customer or user groups from raw data, gaining insights into behavioral patterns and target market strategies.
• 3. AI Business & Financial Modeling
• Created business plans and financial forecasts for the proposed AI solution, including cost estimation, pricing strategy, revenue projections, and value propositions.
• ⸻
• 🎯 Skills Gained:
• Machine Learning (Supervised & Unsupervised)
• Data Preprocessing & Analysis
• Business Modeling & Financial Planning
• Python, Pandas, Scikit-learn, Visualization tools
• Project Documentation & Presentation

Oct 2024 – Oct 2024 · 0 mo

• Recipe Web Page
• Created a responsive recipe web page using HTML and CSS.
• Designed the layout to display recipe details like ingredients, steps, and images clearly.
• Applied CSS styling for a clean, user-friendly interface with attention to colors, fonts, and spacing.
• Gained hands-on experience in frontend development basics and webpage structuring.

Sep 2024 – Sep 2024 · 0 mo

• 📱 What is Mobile Price Prediction?
• It involves using data about various mobile phone specs (like RAM, battery power, screen size, brand, etc.)
• The goal is to predict the price category or price range (e.g., low, medium, high, very high) of a phone before it’s sold.
• 🛠 Common Features Used:
• Battery power
• RAM size
• Internal memory
• Screen resolution
• Camera megapixels
• Brand or manufacturer
• Operating system
• 🔍 What you do in this project:
• Data cleaning and preprocessing: Handle missing data, encode categorical variables
• Exploratory data analysis (EDA): Understand which features influence price most
• Build machine learning models: Logistic Regression, Decision Trees, Random Forest, or XGBoost
• Evaluate models: Use accuracy, confusion matrix, F1 score, etc. to check performance
• Deploy or visualize: Sometimes build a simple app or dashboard to input specs and get price prediction

Jun 2024 – Sep 2024 · 3 mos

• 🍷 What is the Wine Quality Prediction project?
• You use a dataset containing chemical properties of wine samples (like acidity, sugar content, pH, alcohol level, etc.).
• The goal is to predict the quality score or category (usually a score from 0 to 10) assigned to each wine sample.
• 🔍 Key Steps in the Project:
• Data Exploration: Analyze the dataset to understand distributions and relationships between features and wine quality.
• Data Preprocessing: Handle missing values, scale or normalize features if needed.
• Feature Selection: Identify which chemical properties most influence wine quality.
• Model Building: Use machine learning algorithms like Linear Regression, Random Forest, SVM, or XGBoost to predict quality.
• Model Evaluation: Measure performance using metrics like accuracy, RMSE, or classification reports (if treated as classification).
• Visualization: Create charts to show feature importance or quality distributions.
• 🛠 Typical Features Used:
• Fixed acidity
• Volatile acidity
• Citric acid
• Residual sugar
• Chlorides
• Free sulfur dioxide
• Total sulfur dioxide
• Density
• pH
• Sulphates
• Alcohol
• How to describe it for your resume:
• Wine Quality Prediction
• Built a machine learning model to predict wine quality based on chemical attributes.
• Performed data cleaning, feature selection, and exploratory analysis using Python libraries like Pandas and Seaborn.
• Applied regression and classification algorithms to improve prediction accuracy.
• Visualized results and feature importance to interpret the model.

Aug 2023 – Aug 2023 · 0 mo

• A software tool designed to help teams record, track, and manage bugs or issues found during software development and testing. It allows users to log bug details, assign tasks, prioritize fixes, and monitor progress until resolution. The system improves collaboration, ensures timely bug fixes, and helps maintain software quality by organizing all reported issues in one place.

Jun 2023 – Jul 2023 · 1 mo

• Summer Internship – Data Science with Python
• Indian Institute of Technology (IIT) Kanpur
• Completed a certified internship program focused on Data Science using Python conducted by IIT Kanpur.
• Gained practical knowledge of data preprocessing, data visualization, statistics, and machine learning concepts.
• Worked on real-world datasets using NumPy, Pandas, Matplotlib, and Scikit-learn.
• Developed a mini-project involving data analysis and predictive modeling, showcasing understanding of model training, evaluation, and interpretation.
• Strengthened problem-solving skills through hands-on coding exercises and case studies.
• Learned to write clean, efficient, and well-structured Python code for data analysis tasks.

Jun 2023 – Jul 2023 · 1 mo

Aug 2023 – Sep 2023 · 1 mo

• Completed a comprehensive set of LinkedIn Learning courses through TeamSkills, covering a range of in-demand professional and technical skills. The program included modules on topics such as communication, teamwork, time management, web development, Python, React, and other career-focused skills. Strengthened both soft skills and technical abilities through practical exercises, video lessons, and knowledge assessments. All certificates were merged into a single consolidated certification.

Jun 2023 – Jul 2023 · 1 mo

• 🐾 What is Animal Shelter Analysis?
• It typically involves analyzing data from animal shelter records to:
• Understand animal intake and outcome trends
• Predict adoption rates or length of stay
• Identify factors that affect euthanasia vs. adoption
• Improve shelter management and animal welfare
• 📊 Common Dataset Example:
• One famous dataset is the Austin Animal Center Shelter Outcomes dataset (used in many projects and Kaggle competitions).
• It often includes columns like:
• Animal ID, Name, Type (Cat/Dog), Breed, Color
• Intake Type/Date (Stray, Owner Surrender, etc.)
• Outcome Type/Date (Adopted, Euthanized, Transferred, etc.)
• Sex upon Intake/Outcome
• Age upon Intake
• 🔍 Common Analyses Performed:
• Adoption trends over time
• Most adoptable breeds/ages
• Time spent in shelter before adoption
• Correlation between intake type and outcome
• Visualizations (bar charts, heatmaps, pie charts)
• 🛠 Tools Used:
• Python (Pandas, Matplotlib, Seaborn)
• Jupyter Notebooks
• Sometimes machine learning for prediction (e.g., logistic regression, decision trees)