Dajubhai MiniMart Analysis

This repository contains the Jupyter Notebook for dajubhai minimart analysis. The notebook demonstrates a full data science workflow: from data cleaning to model building using a dataset named DajuBhaiMinimart.csv.

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📂 Files

• ITS69304_SupremKhatri_Individual_Assignment.ipynb — Main notebook with code and analysis.
• DajuBhaiMinimart.csv — Dataset (assumed provided externally).

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📝 Notebook Overview

1️⃣ Importing Libraries

The following libraries are used:

import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
import plotly.io as pio
pio.renderers.default = 'notebook'

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2️⃣ Data Loading

df = pd.read_csv('DajuBhaiMinimart.csv')
df.head()

• Reads and displays initial data.

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3️⃣ Data Cleaning

• Checks data info:

  df.info()

• Checks for missing values:

  df.isnull().sum()

• Drops unnecessary columns:

  df.drop('CustomerID', axis=1, inplace=True)

• Renames columns where necessary.

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4️⃣ Data Visualization

• Uses Seaborn and Plotly to visualize relationships and distributions.
• Example plots:

  sns.countplot(x='ProductCategory', data=df)
  px.histogram(df, x='Sales', color='ProductCategory')

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5️⃣ Feature Engineering

• Creates or modifies features to improve model input.
• Handles categorical encoding, scaling, etc.

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6️⃣ Model Training

• Trains models such as:
  • Gradient Boosting Regressor
• Example code:

  from sklearn.ensemble import GradientBoostingRegressor
  model = GradientBoostingRegressor()
  model.fit(X_train, y_train)

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7️⃣ Hyperparameter Tuning & Cross-Validation

• Performs grid search / cross-validation:

  from sklearn.model_selection import GridSearchCV
  params = {'n_estimators': [100, 200], 'learning_rate': [0.1, 0.05]}
  grid = GridSearchCV(GradientBoostingRegressor(), params, cv=5)
  grid.fit(X_train, y_train)

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🚀 How to Run

✅ Clone or download this repository.

✅ Install requirements:

pip install pandas matplotlib seaborn plotly scikit-learn

✅ Run the notebook:

jupyter notebook ITS69304_SupremKhatri_Individual_Assignment.ipynb

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⚠️ Notes

• Ensure DajuBhaiMinimart.csv is present in the notebook directory.
• All visualizations are rendered using Plotly + Seaborn + Matplotlib.
• The notebook suppresses warnings for cleaner output.

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💡 Future Work

• Extend analysis with additional models (e.g. XGBoost, Random Forest).
• Improve feature engineering with domain knowledge.
• Deploy model via Flask / Streamlit for interactive use.