This project is all about exploring a salary dataset to find meaningful patterns - things like how location affects pay, whether company ratings actually matter, and what the overall salary distribution looks like. It covers everything from raw data cleaning to visual insights.

Tools I used : Python, Pandas, Numpy, Seaborn, Matplotlib

Project Workflow :

1. Data Loading

   • Imported the dataset using Pandas and did an initial review of its structure

2. Data Cleaning

   • Removed missing values and duplicates
   • Fixed inconsistent formatting and handled outliers

3. Exploratory Data Analysis (EDA)

   • Summary statistics to understand the data at a glance
   • Distribution analysis to spot skewness or patterns
   • Grouped and aggregated data by location and rating

4. Data Visualization

   • Salary distribution plots
   • Salary by location — which cities/regions pay more
   • Location count — how many job postings exist per location
   • Rating vs. Salary — whether higher-rated companies pay better

Key Insights :

• Salary distribution varies quite a bit across roles and companies
• Location has a strong influence on salary — some regions clearly pay more
• Company ratings show a noticeable trend with salary, though it's not always a perfect relationship
• Locations with very few job postings can skew average salary figures, so count matters too

Conclusion :

This project shows how even a straightforward dataset can reveal interesting patterns once you clean it up and look at it from the right angles. The combination of location, ratings, and count gave a much more complete picture than any single variable on its own.