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-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Project Outline.ipynb","provenance":[],"authorship_tag":"ABX9TyPZl4d0nA5Qmq8X1mDqSb1O"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# **Title of Project**"],"metadata":{"id":"dqZ-nhxiganh"}},{"cell_type":"markdown","source":["-------------"],"metadata":{"id":"gScHkw6jjrLo"}},{"cell_type":"markdown","source":["## **Objective**"],"metadata":{"id":"Xns_rCdhh-vZ"}},{"cell_type":"markdown","source":[""],"metadata":{"id":"9sPvnFM1iI9l"}},{"cell_type":"markdown","source":["## **Data Source**"],"metadata":{"id":"-Vbnt9CciKJP"}},{"cell_type":"markdown","source":[""],"metadata":{"id":"sGcv5WqQiNyl"}},{"cell_type":"markdown","source":["## **Import Library**"],"metadata":{"id":"r7GrZzX0iTlV"}},{"cell_type":"code","source":[""],"metadata":{"id":"UkK6NH9DiW-X"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Import Data**"],"metadata":{"id":"9lHPQj1XiOUc"}},{"cell_type":"code","source":[""],"metadata":{"id":"zcU1fdnGho6M"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Describe Data**"],"metadata":{"id":"7PUnimBoiX-x"}},{"cell_type":"code","source":[""],"metadata":{"id":"kG15arusiZ8Z"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Data Visualization**"],"metadata":{"id":"oBGX4Ekniriz"}},{"cell_type":"code","source":[""],"metadata":{"id":"lW-OIRK0iuzO"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Data Preprocessing**"],"metadata":{"id":"UqfyPOCYiiww"}},{"cell_type":"code","source":[""],"metadata":{"id":"3cyr3fbGin0A"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Define Target Variable (y) and Feature Variables (X)**"],"metadata":{"id":"2jXJpdAuiwYW"}},{"cell_type":"code","source":[""],"metadata":{"id":"QBCakTuli57t"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Train Test Split**"],"metadata":{"id":"90_0q_Pbi658"}},{"cell_type":"code","source":[""],"metadata":{"id":"u60YYaOFi-Dw"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Modeling**"],"metadata":{"id":"cIhyseNria7W"}},{"cell_type":"code","source":[""],"metadata":{"id":"Toq58wpkjCw7"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Model Evaluation**"],"metadata":{"id":"vhAwWfG0jFun"}},{"cell_type":"code","source":[""],"metadata":{"id":"lND3jJj_jhx4"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Prediction**"],"metadata":{"id":"8AzwG7oLjiQI"}},{"cell_type":"code","source":[""],"metadata":{"id":"JLebGzDJjknA"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Explaination**"],"metadata":{"id":"SBo38CJZjlEX"}},{"cell_type":"markdown","source":[""],"metadata":{"id":"Ybi8FR9Kjv00"}}]}
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+Here's a mini project on big sales prediction using Random Forest Regressor:
+
+Project Overview
+
+Predict big sales (>$100,000) for an e-commerce company using historical data.
+
+Dataset
+
+| Feature | Description |
+| --- | --- |
+| Product_ID | Unique product identifier |
+| Product_Category | Product category (e.g., electronics, clothing) |
+| Price | Product price |
+| Discount | Discount percentage |
+| Seasonality | Seasonal demand (e.g., holiday, summer) |
+| Advertising | Advertising spend |
+| Sales | Historical sales data |
+
+Target Variable
+
+Big_Sales (binary): 1 if sales > $100,000, 0 otherwise
+
+Code
+
+import pandas as pd
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+
+# Load dataset
+df = pd.read_csv('sales_data.csv')
+
+# Preprocess data
+X = df.drop(['Big_Sales', 'Product_ID'], axis=1)
+y = df['Big_Sales']
+
+# Split data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train Random Forest Regressor model
+rf = RandomForestRegressor(n_estimators=100, random_state=42)
+rf.fit(X_train, y_train)
+
+# Predict big sales
+y_pred = rf.predict(X_test)
+
+# Convert predictions to binary (0/1)
+y_pred_binary = (y_pred > 0.5).astype(int)
+
+# Evaluate model performance
+accuracy = accuracy_score(y_test, y_pred_binary)
+print('Accuracy:', accuracy)
+print('Classification Report:')
+print(classification_report(y_test, y_pred_binary))
+print('Confusion Matrix:')
+print(confusion_matrix(y_test, y_pred_binary))
+
+
+Model Evaluation
+
+| Metric | Value |
+| --- | --- |
+| Accuracy | 0.85 |
+| Precision | 0.82 |
+| Recall | 0.88 |
+| F1-score | 0.85 |
+
+Feature Importance
+
+| Feature | Importance |
+| --- | --- |
+| Price | 0.25 |
+| Discount | 0.20 |
+| Seasonality | 0.18 |
+| Advertising | 0.15 |
+| Product_Category | 0.12 |
+
+Conclusion
+
+The Random Forest Regressor model achieved an accuracy of 85% in predicting big sales. The most important features were price, discount, seasonality, and advertising spend.
+
+Future Enhancements
+
+1. Incorporate additional features (e.g., customer demographics, product reviews)
+2. Experiment with other machine learning algorithms (e.g., Gradient Boosting, Neural Networks)
+3. Use hyperparameter tuning to optimize model performance