Update Project_Outline.ipynb

Project_Outline.ipynb

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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"}}]}
+Project Title: Mileage Prediction using Regression Analysis
+
+Objective:
+
+1. Develop a model to predict the mileage of a vehicle based on its attributes.
+2. Analyze the relationship between the attributes and mileage.
+3. Evaluate the performance of the model.
+
+Dataset:
+
+- Auto MPG Dataset (available on Kaggle)
+- Attributes:
+    - Cylinders (cyl)
+    - Displacement (disp)
+    - Horsepower (hp)
+    - Weight (wt)
+    - Acceleration (acc)
+    - Model Year (year)
+    - Origin (origin)
+- Target variable: Mileage (mpg)
+
+Methodology:
+
+1. Data Preprocessing:
+    - Handling missing values
+    - Normalization
+2. Feature Selection:
+    - Correlation analysis
+    - Recursive feature elimination
+3. Regression Analysis:
+    - Simple Linear Regression (SLR)
+    - Multiple Linear Regression (MLR)
+    - Polynomial Regression
+    - Ridge Regression
+    - Lasso Regression
+4. Model Evaluation:
+    - Mean Squared Error (MSE)
+    - Mean Absolute Error (MAE)
+    - Coefficient of Determination (R-squared)
+
+Code:
+
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.pipeline import Pipeline
+
+# Load dataset
+df = pd.read_csv('auto-mpg.csv')
+
+# Preprocess data
+df = df.dropna()
+df['origin'] = pd.get_dummies(df['origin'])
+
+# Split data
+X = df.drop('mpg', axis=1)
+y = df['mpg']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Simple Linear Regression (SLR)
+slr = LinearRegression()
+slr.fit(X_train[['cyl']], y_train)
+y_pred_slr = slr.predict(X_test[['cyl']])
+print('SLR MSE:', mean_squared_error(y_test, y_pred_slr))
+print('SLR MAE:', mean_absolute_error(y_test, y_pred_slr))
+print('SLR R-squared:', r2_score(y_test, y_pred_slr))
+
+# Multiple Linear Regression (MLR)
+mlr = LinearRegression()
+mlr.fit(X_train, y_train)
+y_pred_mlr = mlr.predict(X_test)
+print('MLR MSE:', mean_squared_error(y_test, y_pred_mlr))
+print('MLR MAE:', mean_absolute_error(y_test, y_pred_mlr))
+print('MLR R-squared:', r2_score(y_test, y_pred_mlr))
+
+# Polynomial Regression
+poly = PolynomialFeatures(degree=2)
+poly.fit(X_train)
+X_train_poly = poly.transform(X_train)
+X_test_poly = poly.transform(X_test)
+pr = LinearRegression()
+pr.fit(X_train_poly, y_train)
+y_pred_pr = pr.predict(X_test_poly)
+print('Polynomial Regression MSE:', mean_squared_error(y_test, y_pred_pr))
+print('Polynomial Regression MAE:', mean_absolute_error(y_test, y_pred_pr))
+print('Polynomial Regression R-squared:', r2_score(y_test, y_pred_pr))
+
+Challenges:
+
+1. Feature selection
+2. Model selection
+3. Overfitting
+
+Future Work:
+
+1. Incorporate additional features (e.g., engine type, transmission type)
+2. Use more advanced regression techniques (e.g., gradient boosting, random forest)
+3. Develop a web interface for user input and model prediction
+
+References:
+
+1. "Auto MPG Dataset" by UCI Machine Learning Repository
+2. "Regression Analysis" by Wikipedia
+3. "Polynomial Regression" by Scikit-learn Documentation