Sales Forecasting Project
osama zaman
Sales Forecasting
Sales Forecasting
Notebook
Input
Private Dataset
Language
Python
Runtime
Input
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
import warnings
warnings.filterwarnings('ignore')
Load the Data
# Load the CSV file
df = pd.read_csv('/kaggle/input/internet-sales-data/internet_sales.csv', encoding='ISO-8859-1')
# Preview
df.head()
Time Period | Average weekly value for all retailing (£ million) | Average weekly value for Internet retail sales (£ million) | Internet sales as a percentage of total retail sales (%) | |
|---|---|---|---|---|
0 | 2006 Nov | 5573.3 | 155.7 | 2.8 |
1 | 2006 Dec | 6729.0 | 167.9 | 2.5 |
Visualization
plt.figure(figsize=(14, 6))
plt.plot(ts_data, linewidth=2)
plt.title("Internet Retail Sales Over Time", fontsize=16)
plt.xlabel("Date", fontsize=12)
plt.ylabel("Average Weekly Sales (£ million)", fontsize=12)
plt.grid(True)
plt.tight_layout()
plt.show()
Model Fitting
ADF Test
result = adfuller(ts_data.dropna())
print('ADF Statistic:', result[0])
print('p-value:', result[1])
Forecasting
ARIMA Model
# Fit ARIMA(1,1,1)
model_arima = ARIMA(ts_data, order=(1, 1, 1))
model_fit = model_arima.fit()
# Summary of the model
print(model_fit.summary())
Outputs
Tools
Python
ARIMA
SARIMA
Roles
Data Scientist
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Posted May 11, 2026
Developed a sales forecasting model using ARIMA and SARIMA techniques.
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