Instagram Reach Forecasting

Instagram reach forecasting is the process of predicting how many people will see an Instagram post, story, or other content. This can be done using a variety of methods, including machine learning, statistical analysis, and expert opinion. The accuracy of reach forecasting depends on a number of factors, but even a simple model can provide valuable insights into how to improve Instagram reach.

Some of the benefits of Instagram reach forecasting:

• It can help businesses and individuals to optimize their social media strategy.
• It can help to identify the most effective times to post content.
• It can help to determine which hashtags are most effective at reaching the target audience.
• It can help to track the performance of social media campaigns.

Instagram Reach Forecasting with Python

Start this task by importing the necessary Python libraries

import pandas as pd
import plotly.graph_objs as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "plotly_white"

data = pd.read_csv("Instagram-Reach.csv", encoding = 'latin-1')
print(data.head())
data["Date"] = pd.to_datetime(data["Date"])
print(data.head())

#Analyze the trend of Instagram reach over time using a line chart
fig = go.Figure()
fig.add_trace(go.Scatter(x=data["Date"],
                        y=data["Instagram reach"],
                        mode="lines", name="Instagram reach"))
fig.update_layout(title="Instagram Reach Trend", xaxis_title="Date",
                 yaxis_title="Instagram Reach")
fig.show()

Instagram Reach Trend

#Analyze Instagram reach for each day using a bar chart:
fig = go.Figure()
fig.add_trace(go.Bar(x=data["Date"],
                    y=data["Instagram reach"],
                    name="Instagram reach"))
fig.update_layout(title="Instagram Reach by Day",
                 xaxis_title="Date",
                 yaxis_title="Instagram Reach")
fig.show()

Instagram Reach by Day

import numpy as np

day_stats = data.groupby("Day")["Instagram reach"].agg(["mean", "median", "std"]).reset_index()
print(day_stats)
#Create a bar chart to visualize the reach for each day of the week
fig= go.Figure()
fig.add_trace(go.Bar(x=day_stats["Day"],
                    y=day_stats["mean"],
                    name="Mean"))
fig.add_trace(go.Bar(x=day_stats["Day"],
                    y=day_stats["median"],
                    name="Median"))
fig.add_trace(go.Bar(x=day_stats['Day'], 
                     y=day_stats['std'], 
                     name='Standard Deviation'))
fig.update_layout(title='Instagram Reach by Day of the Week', 
                  xaxis_title='Day', 
                  yaxis_title='Instagram Reach')
fig.show()

Instagram Reach by Day the Week

from statsmodels.graphics.tsaplots import plot_pacf
plot_pacf(data["Instagram reach"], lags = 100)
p, d, q = 8, 1, 2

import statsmodels.api as sm
import warnings
model=sm.tsa.statespace.SARIMAX(data["Instagram reach"],
                               order=(p, d, q),
                               seasonal_order=(p, d, q, 12))
model=model.fit()
print(model.summary())

#Make predictions using the model and have a look at the forecasted reach
predictions = model.predict(len(data), len(data)+100)

trace_train = go.Scatter(x=data.index,
                        y=data["Instagram reach"],
                        mode="lines",
                        name="Training Data")
trace_pred = go.Scatter(x=predictions.index,
                       y=predictions,
                       mode="lines",
                       name="Predictions")

layout = go.Layout(title="Instagram Reach Time Series and Predictions",
                  xaxis_title="Date",
                  yaxis_title="Instagram Reach")

fig = go.Figure(data=[trace_train, trace_pred], layout=layout)
fig.show()

Instagram Reach Time Series and Predictions

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