Instagram_Reach_Forecasting

This repository provides a structured approach to analyzing and forecasting Instagram reach using historical data. The primary goal is to identify patterns, trends, seasonality, and anomalies to develop a predictive model capable of forecasting future reach. The analysis and forecasting are implemented using Python and several data analysis libraries.

Dataset

The dataset, Instagram-Reach.csv, includes:

• Date: The date of the Instagram post.
• Instagram Reach: The number of people reached by the post on the corresponding date.

Project Structure

• data/: Contains the dataset.
• notebooks/: Jupyter notebooks with analysis and forecasting steps.
• scripts/: Python scripts for data analysis and modeling.
• results/: Output plots and forecast results.

Analysis and Forecasting Steps

1. Import Data and Initial Checks

• Import the dataset.
• Check for null values, column info, and descriptive statistics.

2. Convert Date Column to Datetime

• Convert Date to datetime datatype.
• Set Date as the index.

3. Trend Analysis

• Plot a line chart to visualize Instagram reach trends over time.

4. Daily Reach Analysis

• Plot a bar chart for daily Instagram reach.

5. Distribution Analysis

• Create a box plot to visualize Instagram reach distribution.

6. Weekly Reach Analysis

• Create a Day column from the Date column.
• Group by day and calculate mean, median, and standard deviation of Instagram reach.

7. Weekly Reach Visualization

• Plot a bar chart for mean Instagram reach by day of the week.

8. Seasonal Decomposition

• Decompose the time series to analyze trends and seasonal patterns.

9. SARIMA Model Parameter Determination

• Use autocorrelation and partial autocorrelation plots to determine SARIMA parameters (p, d, q).

10. SARIMA Model Training and Forecasting

• Train a SARIMA model and make predictions.
• Plot historical data and forecasted values.

Requirements

Install required libraries using:

pip install pandas numpy matplotlib seaborn statsmodels