Coder_Roots Assignment

This repository contains solutions to three tasks involving data manipulation, visualization, and predictive modeling. Each task demonstrates specific data science techniques using Python.

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Task 1: Data Manipulation and Cleaning

Objective:

Clean and analyze the employee_data.csv dataset.

Dataset:

Employee_Data

Steps:

1. Remove duplicate entries.
2. Handle missing values (fill them with default values or drop the rows).
3. Convert the JoiningDate column to a proper datetime format.
4. Filter out employees where the Status is "Resigned".
5. Analyze the data:
   • Find the average salary by department.
   • List employees who joined after 2020.

Code:

Refer to the script Task1_Data Manipulation and Cleaning.ipynb.

Outputs:

• Cleaned DataFrame.
• Average salary per department.
• List of employees who joined after 2020.

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Task 2: Data Visualization

Objective:

Explore a public dataset through visualizations.

Dataset:

Any public dataset can be used (e.g., Titanic dataset). Titanic dataset

Steps:

1. Load the dataset into a Pandas DataFrame.
2. Create four meaningful visualizations using Matplotlib or Seaborn.
   • Examples: Bar plots, histograms, box plots, etc.
3. Generate a correlation heatmap for numerical variables.

Code:

Refer to the script Task2_Data Visualization.ipynb.

Outputs:

• Visualizations:
  1. Survival rate by passenger class.
  2. Histogram of passenger ages.
  3. Box plot of fare distribution.
  4. Correlation heatmap.

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Task 3: Predictive Modeling (Classification)

Objective:

Build a classification model to predict the presence of diabetes based on health metrics.

Dataset:

Diabetes Prediction Dataset

Steps:

1. Data Preprocessing:
   • Replace missing or undefined values.
   • Convert categorical variables to numerical using encoding techniques.
   • Normalize or scale features if necessary.
2. Model Building:
   • Split the dataset into training and testing sets.
   • Train two classification models:
     1. Logistic Regression
     2. Decision Tree
3. Model Evaluation:
   • Evaluate models using accuracy, precision, recall, and F1 score.

Code:

Refer to the script Task3_Predictive Modeling.ipynb.

Outputs:

• Preprocessed dataset.
• Performance metrics for Logistic Regression and Decision Tree models:
  • Accuracy, Precision, Recall, F1 Score.

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Installation and Usage

1. Clone the repository:

   git clone https://github.com/AbhinavSharma07/Coder_Roots.git
   cd Coder_Roots