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# Dataset | ||
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## Overview | ||
This folder contains the dataset used for the Salary Prediction of Data Professionals project. | ||
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## Contents | ||
- `Salary Prediction of Data Professions.csv`: The main dataset used for training and testing the model. | ||
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## Description | ||
The dataset includes various features such as job role, experience, age, ratings, and other relevant attributes to predict the salary of data professionals. |
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# Images | ||
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## Overview | ||
This folder contains images generated during different stages of the project such as data preprocessing, exploratory data analysis (EDA), feature engineering, and model evaluation. | ||
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## Structure | ||
- `.datapreprocessing`: Images related to data preprocessing steps. | ||
- `.data_handling`: Images showing data handling techniques. | ||
- `.eda`: Visualizations created during exploratory data analysis. | ||
- `.feature_engineering`: Images showing feature engineering steps. | ||
- `.Machine_Learning_Model_Development_and_Evaluation`: Visualizations related to model development and evaluation. | ||
- `.ml_pipeline`: Images depicting the machine learning pipeline. | ||
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# Model | ||
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## Overview | ||
This folder contains the main project files for the Salary Prediction of Data Professionals project. | ||
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## Contents | ||
- `mentorness_ml_1.ipynb`: The Jupyter notebook file containing the entire workflow of the project including data analysis, feature engineering, model training, and evaluation. | ||
- `README.md`: This readme file. | ||
- `data`: A folder containing the dataset used in the project. | ||
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## Instructions | ||
1. Open the `mentorness_ml_1.ipynb` to view the step-by-step workflow. | ||
2. Install the necessary dependencies listed in the `requirements.txt` file. |
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