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https://github.com/himel-sarder/ml-exercise-sales-prediction-advertising-simple-linear-regression
This project analyzes Kaggle's Advertising Dataset to predict sales based on advertising budgets for TV, Radio, and Newspaper. Using linear regression, it explores relationships between spending and sales, evaluates model performance, and provides actionable insights for data-driven marketing decisions.
https://github.com/himel-sarder/ml-exercise-sales-prediction-advertising-simple-linear-regression
linear-regression machine-learning machine-learning-algorithms machinelearning ml regression-analysis regression-models sales-prediction sales-prediction-using-python simple-linear-regression
Last synced: 9 days ago
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This project analyzes Kaggle's Advertising Dataset to predict sales based on advertising budgets for TV, Radio, and Newspaper. Using linear regression, it explores relationships between spending and sales, evaluates model performance, and provides actionable insights for data-driven marketing decisions.
- Host: GitHub
- URL: https://github.com/himel-sarder/ml-exercise-sales-prediction-advertising-simple-linear-regression
- Owner: Himel-Sarder
- License: mit
- Created: 2024-12-25T17:17:39.000Z (15 days ago)
- Default Branch: main
- Last Pushed: 2024-12-25T18:52:43.000Z (15 days ago)
- Last Synced: 2024-12-25T19:28:51.447Z (15 days ago)
- Topics: linear-regression, machine-learning, machine-learning-algorithms, machinelearning, ml, regression-analysis, regression-models, sales-prediction, sales-prediction-using-python, simple-linear-regression
- Language: Jupyter Notebook
- Homepage:
- Size: 103 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ML-Exercise-Advertising-Simple-Linear-Regression
Dataset - https://www.kaggle.com/code/ashydv/sales-prediction-simple-linear-regression/input
# Advertising Dataset Analysis
## Project Overview
This project involves analyzing Kaggle's Advertising Dataset to build a simple linear regression model that predicts sales based on advertising spend. The dataset includes columns for TV, Radio, Newspaper advertising budgets, and Sales.
## Author
- **Name**: Himel Sarder
- **Email**: [email protected]
- **GitHub**: [Himel-Sarder](https://github.com/Himel-Sarder)
## Objective
The goal of this project is to:
1. Explore the relationship between advertising spend on TV and sales.
2. Build a linear regression model to predict sales based on TV advertising.
3. Evaluate the model's performance using statistical metrics.
## Dataset
The dataset contains the following columns:
- **TV**: Advertising budget for TV (in thousands of dollars).
- **Radio**: Advertising budget for Radio (in thousands of dollars).
- **Newspaper**: Advertising budget for Newspaper (in thousands of dollars).
- **Sales**: Sales generated (in thousands of units).
### Sample Data
| TV | Radio | Newspaper | Sales |
|--------|--------|-----------|-------|
| 230.1 | 37.8 | 69.2 | 22.1 |
| 44.5 | 39.3 | 45.1 | 10.4 |
| 17.2 | 45.9 | 69.3 | 12.0 |
| 151.5 | 41.3 | 58.5 | 16.5 |
| 180.8 | 10.8 | 58.4 | 17.9 |
## Steps
### 1. Import Libraries
The following Python libraries are used:
- `warnings` to suppress warnings.
- `matplotlib.pyplot` and `seaborn` for data visualization.
- `pandas` and `numpy` for data manipulation.
- `math` for mathematical operations.
- `sklearn` for machine learning tasks.
- `statsmodels` for statistical modeling.
### 2. Load the Dataset
The dataset is loaded using:
```python
import pandas as pd
df = pd.read_csv('advertising.csv')
```
### 3. Exploratory Data Analysis (EDA)
- Scatter plot to visualize the relationship between TV advertising and Sales:
```python
plt.figure(figsize=(12, 8))
plt.scatter(df['TV'], df['Sales'])
plt.xlabel('TV')
plt.ylabel('Sales')
plt.show()
```
### 4. Data Preparation
- Features (`X`) and target (`y`) are defined as:
```python
X = df.iloc[:, 0:1] # TV column
y = df.iloc[:, -1] # Sales column
```
- Split the dataset into training and testing sets:
```python
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100)
```
### 5. Model Training
- A linear regression model is trained using:
```python
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, y_train)
```
### 6. Model Evaluation
- The model's performance is evaluated using:
- **Mean Squared Error (MSE)**
- **Root Mean Squared Error (RMSE)**
- **R-squared**
```python
from sklearn.metrics import mean_squared_error
predictions = lr.predict(X_test)
rmse = sqrt(mean_squared_error(y_test, predictions))
print(f"RMSE: {rmse}")
```
- The regression equation is printed:
```python
print(f"The linear model is: Y = {lr.intercept_:.5f} + {lr.coef_[0]:.5f}X")
```
### 7. Statistical Analysis
- The model is refitted using `statsmodels` for detailed statistical insights:
```python
import statsmodels.api as sm
X_train_sm = sm.add_constant(X_train)
lr_sm = sm.OLS(y_train, X_train_sm).fit()
print(lr_sm.summary())
```
### 8. Error Analysis
- Residuals are analyzed to ensure the model's assumptions are met:
```python
res = y_train - lr_sm.predict(X_train_sm)
sns.distplot(res, bins=15)
plt.title('Error Terms')
plt.xlabel('y_train - predictions')
plt.show()
```
## Results
- The regression equation is:
```
Y = 6.94868 + 0.05455X
```
- Key performance metrics:
- **RMSE**: 2.019
- **R-squared**: 0.816
- The model explains approximately 81.6% of the variance in sales based on TV advertising.
## Conclusion
- TV advertising has a significant positive relationship with sales.
- The linear regression model is effective in predicting sales based on TV advertising spend.
## References
- Kaggle's Advertising Dataset
- Python libraries: `sklearn`, `statsmodels`, `matplotlib`, `seaborn`, `pandas`, `numpy`
---
For more details, visit [GitHub](https://github.com/Himel-Sarder).