Project Details

In this project, I created a sales report using excel pivot tables which will enable businesses to determine effective customer discounts, facilitate negotiations with consumers, and identify potential business expansion opportunities in promising countries. The reports generated will provide actionable insights and recommendations for businesses to optimize their sales strategies, enhance customer satisfaction, and maximize revenue generation.

I have learned the following:

1. Pivot table
2. Power Query
3. DAX (Basics)
4. Conditional Formatting
5. Report Beautification
6. Sales Domain Metrics

Overall, this project aims to empower businesses with the necessary tools and insights to monitor, evaluate, and enhance their sales activities. By leveraging sales data analysis, businesses can unlock new opportunities, drive growth, and stay competitive in the dynamic market landscape.

Project Details

The primary goal of the project is to develop a model that can accurately classify the sentiment expressed in tweets. This is achieved by processing the textual content of the tweets and using machine learning techniques to determine whether the sentiment is positive, negative, or neutral. Such classification is essential for businesses and researchers who want to analyze public reactions, customer satisfaction, or trends in social media discussions.

Tools & Libraries:
The project leverages several Python libraries:

Pandas: For efficient data manipulation and analysis.
scikit-learn: For implementing machine learning algorithms, including the Multinomial Naive Bayes classifier.
NLTK: For performing NLP tasks such as tokenization, stopword removal, and stemming.
Count Vectorizer: For converting text data into numerical features that the machine learning model can process.

Project Details

In this project, the goal is to be one report which could be used by stakeholders from sales, marketing, finance and executive team. The focus is made on the following

1. Robust Data Modeling
2. User-empathetic Report design
3. Drillable Insights

I learned the following things in this project

1. Power Query (Basic and Advanced Operations)
2. Basic and complex DAX formulas
3. Data modelling involving 10+ tables
4. Choosing the right visuals and formatting
5. Dashboard designing principles
6. Using bookmarks
7. Deploying in Power BI service
8. Stakeholder Feedback Implementation
9. Sales, Marketing, Finance & Supply Chain metrics

Live Interactive Dashboard

Project Details

nsupervised Classification for Customer Segmentation
Description
This project leverages unsupervised learning techniques to segment customers based on their spending behavior and annual income. Using K-Means clustering, the project identifies distinct customer groups by analyzing their Annual Income (k$) and Spending Score (1-100).

Key Components
Data Analysis: Exploration and preprocessing of customer data to prepare for clustering.
K-Means Clustering: Application of the K-Means algorithm to group customers into clusters based on their financial and spending attributes.
Visualization: Use of Seaborn and Matplotlib to create scatter plots that illustrate the clusters and their centers. These visualizations help in understanding the distribution of customers and the characteristics of each cluster.
Cluster Interpretation: Analysis of cluster characteristics to derive meaningful insights and labels for each customer segment.

Project Details

Atliq Hardware, a major computer hardware manufacturer, needed to improve data-driven decision-making.
Approach:
Used MySQL for advanced querying (CTEs, window functions, etc.) to analyze key metrics like Gross Profit, Contribution %, and Average Sales.
Created insightful visuals in PowerBI, including Sunburst Charts, Bar Charts, and Maps.
Solutions:
Proposed increasing production rates and attracting customers with memberships, gift certificates, bundled deals, and discounts.
Key Learnings:
Mastered advanced SQL techniques and analytical querying.
Improved skills in translating SQL queries into actionable visual insights.
Enhanced presentation skills for effectively communicating data findings.