sales.prediction.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# Load dataset
data = pd.read_csv("sales_data.csv")

# Convert Date column to datetime
data['Date'] = pd.to_datetime(data['Date'])

# Convert date to numerical value (number of days)
data['Days'] = (data['Date'] - data['Date'].min()).dt.days

# Input (X) and Output (y)
X = data[['Days']]
y = data['Sales']

# Create model
model = LinearRegression()

# Train model
model.fit(X, y)

# Predict future sales (next 5 days)
future_days = np.array([16,17,18,19,20]).reshape(-1,1)
predicted_sales = model.predict(future_days)

print("Predicted Sales for Next 5 Days:")
for i, sale in enumerate(predicted_sales):
    print("Day", future_days[i][0], ":", int(sale))

# Plot graph
plt.scatter(data['Days'], data['Sales'], color='blue', label='Actual Sales')
plt.plot(data['Days'], model.predict(X), color='red', label='Regression Line')

plt.xlabel("Days")
plt.ylabel("Sales")
plt.title("Sales Forecasting using Machine Learning")
plt.legend()

plt.show()