Salary Prediction

In this project, I've initially performed EDA steps to explore and clean the data. Then I've build a model that predicts salaries based on various parameters like industry, yearsExperience, Degree, etc.

Exploratory Data Analysis

For the exploratory analysis I first checked for duplicate or missing values, then I merged the test and training data into a sigle data frame. I performed outlier analysis to remove potential outliers. Finally I found that below variable have good correlation with salary:

• jobType
• degree
• industry
• major
• yearsExperience
• milesFromMetropolis

Model Summary

I ran 4 model LinearRegression, make_pipeline(which basically standardizes the data, applies PCA and the Linear regresssion on entire data), RandomForestRegressor and GradientBoostingRegressor. Finally I used Mean Squared Errors are performace evaluation criteria for all the models. Below is an overall summary of all the models.

Algorithm	MSE
Pipeline	384.48
LinearRegression	384.47
RandomForestRegressor	367.93
GradientBoostingRegressor	357.43

From the MSE values, I concluded that GB model has the best prediction accuracy, hence I used this algorithm to predict salaries in train dataset.

Finally, I created a barplot to show importance of each feature

Markup :

Note:

Github wont allow me to upload the data as it is very big.

The data can be found at: https://www.dropbox.com/sh/i9rdy7obsxcri1p/AAARI7cfTQHKHySKMyAY9a66a?dl=0

Drop me a request if you're not able to download the data