###**WEEK 1 project
As a data scientist working ar instagram how would you analyse key performance insights in assessing the success of the IGTV product

###**Step 1:
**Description
As a data scientist at instagram i would follow the data science process that would involve fundamental of statistcs, mathematics and programming

###**Step 2:Data Collection
I generated my data from mockaroo as a csv file with various fields
```python
import pyforest
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt 
import seaborn as sbs
pd.set_option('display.max_rows',None)

data=pd.read_csv("C:/Users/User/Downloads/MOCK_DATA (2).csv")
```

###**Step 3:Data Preprocessing
This is how i cleaned the data.
```python
data.dtypes
data.head()
data.shape
df=data.drop(['impressions'],axis=1)
```

###Renaming
```python

df=df.rename(columns={'video_id':'Video_ID','views':'No_of_Views','average_watch_time':'Average_WatchTime','completion_rate':'Completion_Rate','click_through_rate':'Clicks_Rate','engangement_rate':'Engagement_Rate'})
df
```
###drop null values
```python
df=df.drop(df.isna(),axis=1)
```
df.loc[df.duplicated()]

```

###**Step 4:Data Exploration
This is how I performed EDA using diffrent approaches
```python
df_sorted = df.sort_values('followers', ascending=False)
mostly_followed = df[:10]
mostly_followed
# Create a horizontal bar chart
plt.barh(mostly_followed['Video_ID'], mostly_followed['followers'])
plt.xlabel('Followers')
plt.ylabel('Video ID')
plt.title('Top 10 Videos by Followers')

plt.show()


```

###**Step 5:Predictive Modelling
I analysed two models
1)
```python
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
# Creating a copy of the DataFrame 
most_influential_copy = most_influential.copy()

# Handling missing values (NaN) in the 'shares' column in the copied DataFrame
most_influential_copy['shares'].fillna(0, inplace=True)

# Defining independent variables (X) and dependent variable (y)
X = most_influential[['likes', 'comments', 'shares']]
y = most_influential['Video_ID']

# Splitting the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)


model = LinearRegression()

# Fitting the model to the training data
model.fit(X_train, y_train)

# Making predictions on the test data
y_pred = model.predict(X_test)

# Evaluating the model
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f"Mean Squared Error (MSE): {mse:.2f}")
print(f"R-squared (R2): {r2:.2f}")

# Coefficients and intercept
coefficients = model.coef_
intercept = model.intercept_

print(f"Coefficients: {coefficients}")
print(f"Intercept: {intercept}")


```

2)
```python
#using random forest
from sklearn.ensemble import RandomForestRegressor

da=pd.read_csv("C:/Users/User/Downloads/MOCK_DATA (2).csv")
da
da=data.drop(['impressions'],axis=1)
da.fillna(da.mean(), inplace=True)
da['average_watch_time']=pd.to_datetime(da['average_watch_time'])
# Splitting the data into features (X) and target (y)
X = da.drop('average_watch_time', axis=1)  
y = da['engagement_rate']  

# Split the data into training and testing sets (70% train, 30% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Random Forest Regressor model
model = RandomForestRegressor(n_estimators=100, random_state=42)  # You can adjust hyperparameters as needed

# Fitting the model on the training data
model.fit(X_train, y_train)

# Making predictions on the test data
y_pred = model.predict(X_test)

# Evaluating the model's performance
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f"Mean Squared Error: {mse}")
print(f"R-squared: {r2}")

```

###**Step 6:Selection of the models
I decided to go with the random forest model as it yielded positive prediction and used it in my rest ot the data.
Random Forest:
Mean Squared Error: 0.00918879593299292
R-squared: 0.99998861412386
Linear Regression:
Mean Squared Error (MSE): 91429.19
R-squared (R2): -3.41
Coefficients: [ 0.79901879 -0.02785449 -0.05815464]
Intercept: -79012.17522320009