Startup_Success_Predictor

Startup Profit Prediction Using Multiple Linear Regression

This project demonstrates the use of Multiple Linear Regression to predict the profits of startups based on investment in R&D, Administration, and Marketing. The dataset (50_Startups.csv) includes 50 records of startups with features such as R&D Spend, Administration, Marketing Spend, State, and Profit. The analysis preprocesses the dataset (including encoding categorical data), splits it into training and testing sets, and builds a predictive model using Python libraries like pandas, NumPy, and scikit-learn. Insights on model performance are obtained using evaluation metrics such as R-squared.

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Description of the Dataset

The 50_Startups.csv dataset contains information about 50 startups, with the following attributes:

R&D Spend: Investment in research and development. ($) // Administration: Investment in administrative operations. ($) // Marketing Spend: Budget allocated for marketing campaigns. ($) // State: The state where the startup operates. (New York, California, or Florida) // Profit: The target variable, representing the net profit of the startup. ($) //

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Key Insights :

Profit and R&D Spend: Higher R&D investments often correlate with higher profits, suggesting it is a crucial factor. State Influence: The dataset includes three categorical states. Encoding this variable helps explore its influence on profit. Balanced Features: All numeric columns represent monetary values, making preprocessing straightforward without scaling.

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Key Steps

1. Data Preparation: Cleaned and encoded categorical data (State), then split into independent variables (X) and the dependent variable (Profit).
2. Model Building: Trained a Multiple Linear Regression model using LinearRegression from scikit-learn.
3. Prediction and Evaluation: Predicted startup profits on test data and assessed performance metrics.

Run the notebook to reproduce the workflow or adapt the model for similar regression problems.

Metrics

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Code :

import pandas as pd
from nbformat import read

# Paths to the uploaded files
notebook_path = '/mnt/data/Multiple_Linear_Regression.ipynb'
dataset_path = '/mnt/data/50_Startups.csv'

# Load and analyze the Jupyter notebook content
with open(notebook_path, 'r', encoding='utf-8') as f:
    notebook = read(f, as_version=4)

# Load the dataset to inspect its structure
dataset = pd.read_csv(dataset_path)

# Extract the notebook structure
code_cells = [cell['source'] for cell in notebook['cells'] if cell['cell_type'] == 'code']
markdown_cells = [cell['source'] for cell in notebook['cells'] if cell['cell_type'] == 'markdown']

# Get dataset summary and notebook insights
dataset_info = {
    "columns": dataset.columns.tolist(),
    "shape": dataset.shape,
    "head": dataset.head().to_dict()
}

notebook_info = {
    "total_cells": len(notebook['cells']),
    "code_cells_count": len(code_cells),
    "markdown_cells_count": len(markdown_cells),
    "example_code_snippet": code_cells[:2],  # First two code cells for reference
    "example_markdown": markdown_cells[:2]  # First two markdown cells for reference
}

dataset_info, notebook_info

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Connect :

If you have any questions or suggestions, feel free to reach out to me:

• Email: vijaykumarit45@gmail.com
• GitHub: Profile
• Linkedin: Linkedin
• Twitter: Twitter

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