Portfolio

This portfolio is a compilation of some of the major data analytics projects I have done during my learning journey.

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Projects:

Project 1: Wearable Device Usage Analysis: Uncovering Health & Activity Patterns

Overview: Analyzed Fitbit smart device data to uncover trends in user activity, sleep, and sedentary behavior, and translated findings into actionable product and marketing recommendations for Bellabeat — a health-tech company creating smart wellness products for women.

Goal: Identify trends in smart device usage, apply them to Bellabeat’s customer base, and shape a data-driven marketing strategy for the Bellabeat Time wellness watch.

Skills: EDA, SQL (DDL, DML, CTEs, Window Functions, CASE Statements, Joins), Dashboard Design (LOD Expressions, Calculated Fields, Parameters, Layout Containers, Dynamic Filters, Light/Dark Mode), Data Storytelling

Tools Used: MS SQL Server, Tableau, Visual Studio Code, GitHub Copilot

Results:

1. Users average 7,500 steps/day — 25% below WHO’s 10,000 step recommendation — with 81% of daily time spent sedentary (16.4 hrs).
2. Average sleep is 6.3 hrs/night, creating a 10.5 hr weekly sleep deficit; users lose 42 mins nightly lying awake in bed.
3. Identified 54% of users as lightly active — the prime Bellabeat target segment — with peak activity occurring in the evening (5–7 PM).
4. Recommended 3 Bellabeat product features: 60-min inactivity alert, bedtime wind-down notification, and weekday habit-building challenges.
5. Proposed marketing strategy: lead with the sleep gap story and schedule campaigns at 5–7 PM to align with peak user motivation.

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Project 2: Customer Segmentation Analysis

Overview: This project focusses on segmenting customer base using various data analysis technique to gain insights into their behavior, preferences and response to marketing campaigns.

Goal: To help business better understand their customers and tailor their marketing strategies for maximum effectiveness.

Skills: EDA, data transformations and aggregations, customer segmentations, RFM Analysis (Recency, Frequency, Monetary Value), Data Visualization.

Tools Used: Python (libraries used- Pandas, Matplotlib, Seaborn, Plotly)

Result:

1. Targeted campaigns based on income segments.
2. Improving campaign effectiveness.
3. Retaining key customer segments, leveraging demographic insights.

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Project 3: Electric Vehicle Market Analysis

Overview: This project analyzes Electric Vehicle market in India, focusing on market trends, sales growth and revenue projections.The analysis aims to support AtliQ Motors in understanding the current EV landscape and informing their expansion strategy in the Indian market.

Goal:

1. Analyze EV sales data to identify growth trends.
2. Determine the competitive landscape in the Indian EV market.
3. Recommend optimal Indian states for launching new EV products.

Skills: data collection, data cleaning, SQL( DDL, DML, CTEs, etc), Mathematical techniques, Data Visualization, Powerpoint Report.

Tools Used: MS SQL Server, Tableau, Powerpoint.

Result:

1. Growth Trends: Significant growth observed in the 2-Wheeler and 4-Wheeler segments from 2022 to 2024.
2. Market opportunities: Identified Indian states with high potential for new EV launches.
3. Competitor landscape: Overview of key competitors and their market share.

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Project 4: Book Recommendation System

Overview: This project is a Content-based Book Recommendation System that suggests books similar to the one provided by the users. It analyzes various features of the input book and finds the most relevant matches using text-based similarity techniques.

Goal: To built a content-based filtering system that recommends books similar to a given book title using metadata such as the books title, author, description, etc.

Skills: Data cleaning & preprocessing, Text Vectorization, Similarity measurement, Text Processing with NLTK, Feature Engineering, EDA

Tools Used: Python ( Pandas – for data manipulation, Seaborn & Matplotlib – for data visualization ,scikit-learn – for cosine similarity, MinMaxScaler, and TfidfVectorizer, NLTK – for text preprocessing (stopwords, lemmatization))

Result:

1. Sucessfully recommends top 10 books similar to a given title.
2. Tested with several book titles- results were relevant, meaningful, and aligned well with input themes.
3. Effecient performance even on a large dataset with thousands of books.

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Project 5: Coffee Beans Sales Dashboard

Overview: This project features a Dynamic Sales Dashboard built in Microsoft Excel to analyze and visualize sales performance data for a Coffee Beans business. It provides valuable insights into total sales and profit by country-wise and time-based to support data-driven decisions.

Goal: To design an interactive excel dashboard that allows stakeholders to monitor key sales metrics, identify top performing products/ cities and track sales trends over time.

Skills: Data aggregation, Dashboard design, metric building, data analysis.

Tools Used: Microsoft Excel (Pivot Tables & Pivot Charts, Slicers for interactivity, etc.)

Result:

1. Identified Profit by Coffee Type and Profit % by packet size.
2. Identified top 3 best performing cities.