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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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+ mileage prediction project using regression analysis in Python
+
+Dataset:
+For this example, we'll use a sample dataset containing information about various car models, including their features and mileage. You can replace this dataset with your own.
+
+Dataset Columns:
+
+- Model_Year
+- Cylinders
+- Displacement
+- Horsepower
+- Weight
+- Acceleration
+- Mileage (target variable)
+
+Code:
+
+# Import necessary libraries
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error, r2_score
+
+# Load dataset
+data = {
+    'Model_Year': [2015, 2018, 2020, 2012, 2019, 2016, 2017, 2014, 2021, 2013],
+    'Cylinders': [4, 6, 4, 6, 4, 8, 4, 6, 4, 8],
+    'Displacement': [2.5, 3.5, 2.0, 4.0, 2.5, 5.0, 2.5, 3.5, 2.0, 5.0],
+    'Horsepower': [150, 250, 180, 300, 200, 350, 220, 280, 200, 320],
+    'Weight': [3000, 4000, 2800, 4500, 3200, 5000, 3000, 4200, 2800, 4800],
+    'Acceleration': [8.5, 6.5, 7.5, 5.5, 7.0, 4.5, 8.0, 6.0, 7.5, 5.0],
+    'Mileage': [25, 20, 28, 18, 24, 15, 26, 22, 29, 19]
+}
+
+df = pd.DataFrame(data)
+
+# Explore dataset
+print(df.head())
+print(df.describe())
+
+# Split dataset into features (X) and target (y)
+X = df.drop('Mileage', axis=1)
+y = df['Mileage']
+
+# 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)
+
+# Linear Regression Model
+lr_model = LinearRegression()
+lr_model.fit(X_train, y_train)
+y_pred_lr = lr_model.predict(X_test)
+
+# Random Forest Regressor Model
+rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
+rf_model.fit(X_train, y_train)
+y_pred_rf = rf_model.predict(X_test)
+
+# Evaluate models
+print("Linear Regression Metrics:")
+print(f"MSE: {mean_squared_error(y_test, y_pred_lr)}")
+print(f"R2 Score: {r2_score(y_test, y_pred_lr)}")
+
+print("Random Forest Regressor Metrics:")
+print(f"MSE: {mean_squared_error(y_test, y_pred_rf)}")
+print(f"R2 Score: {r2_score(y_test, y_pred_rf)}")
+
+# Plot predicted vs actual mileage
+plt.scatter(y_test, y_pred_lr, label='Linear Regression')
+plt.scatter(y_test, y_pred_rf, label='Random Forest Regressor')
+plt.xlabel('Actual Mileage')
+plt.ylabel('Predicted Mileage')
+plt.legend()
+plt.show()
+
+Explanation:
+
+1. Import necessary libraries.
+2. Load the dataset.
+3. Explore the dataset using head() and describe().
+4. Split the dataset into features (X) and target (y).
+5. Split data into training and testing sets using train_test_split().
+6. Train a Linear Regression model and a Random Forest Regressor model on the training data.
+7. Make predictions on the testing data.
+8. Evaluate the models using Mean Squared Error (MSE) and R2 Score.
+9. Plot the predicted vs actual mileage for both models.
+
+Advice:
+
+- Use a larger dataset for better results.
+- Feature engineer additional variables (e.g., engine type, transmission type).
+- Experiment with different regression algorithms (e.g., Ridge, Lasso, Elastic Net).
+- Tune hyperparameters for optimal performance.
+- Consider using cross-validation for more robust evaluation.
+
+Example Use Cases:
+
+- Predicting fuel efficiency for new car models.
+- Identifying key factors affecting mileage.
+- Comparing the performance of different regression algorithms.
+- Developing a mileage prediction tool for car manufacturers or consumers