README.md

Movies Correlation in Python

This projects aims to find what fields are directly correlated with the gross revenue of a movie. It will be interesting to know what impcts the revenue of a film.

Import libraries

The libraries that are going to be used are pandas, numpy for data manipulation and transforming, matplotlib and seaborn for visuals.

import pandas as pd
import numpy as np
import seaborn as sns

import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import matplotlib
plt.style.use('ggplot')
from matplotlib.pyplot import figure

%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (12,8)

pd.options.mode.chained_assignment = None

# Now we need to read in the data
df = pd.read_csv(r'https://raw.githubusercontent.com/sndaba/MoviesCorrelationInPython/main/movies.csv')

Data Cleaning

1. Missing Values

The missing data section sees which field has missing data by percent. The for loop goes through the dataset to see if it has nulls in it

for col in df.columns:
    pct_missing = np.meandf.[col].isnull()]
    print('{} - {}%.format(col, pct_missing))

2. Change data type

Changing "gross" and "budget" columns to make them whole numbers

df['budget'] = df['budget'].astype['int64']
df['gross'] = df['gross'].astype['int64']

3. Drop duplicates

Drop any duplicate existing in the dataset

df.drop_duplicates()

4. Sorting by 'gross' column

Order the data to see the highest grossing movies by 'gross' column

df.sort_values(by=['gross'], inplace=False, ascending=False)

Finding Correlations

Before starting to find any correlations, the hypothesis is that 'budget', 'company' have a high correlation. These are columns to test with the 'gross' column.

1. 'budget' and 'gross' column scatter plot

A scatter plot can be used to show the 'budget' and 'gross' using matplotlib

plt.scatter(x=df['budget'], y=df['gross'])
plt.title('Budget vs Gross Earnings')
plt.xlabel('Gross Earnings')
plt.ylabel('Budget for film')
plt.show

2. Correlation between 'budget' and 'gross'

The scatter plot kind of shows whether the 'gross' and 'budget' are visually correlated. However, its time to determine whether they arecorrelated using the regplot(regression plot) using seaborn.

sns.regplot(x="gross", y="budget", data=df, scatter_kws={"color":"red"}, line_kws={"color":"blue"})

3. Correlation Matrix between all numeric columns

Using pearson, kendall and spearman. However, keeping to the default 'pearson'.

df.corr(method ='pearson') #pearson, kendall and spearman

#results show high correlation between 'budget' and 'gross'

4. Correlation matrix visual

Using seaborn and matplotlib

correlation_matrix = df.corr()

sns.heatmap(correlation_matrix, annot = True)

plt.title("Correlation matrix for Numeric Features")

plt.xlabel("Movie features")

plt.ylabel("Movie features")

plt.show()

5. Numerise all columns

The 'company' data type is set as an object. To convert this, it is best to change it to category data type. The other coulumns that are already numeric, will be left alone, that is, they wil not be changed.

df_numerized = df

for col_name in df_numerized.columns:
    if(df_numerized[col_name].dtype == 'object'):
        df_numerized[col_name]= df_numerized[col_name].astype('category')
        df_numerized[col_name] = df_numerized[col_name].cat.codes
        
df_numerized

6. Visualisation of the numerised columns

correlation_matrix = df_numerized.corr(method='pearson')

sns.heatmap(correlation_matrix, annot = True)

plt.title("Correlation matrix for Movies")

plt.xlabel("Movie features")

plt.ylabel("Movie features")

plt.show()

7. Correlation of numerised columns

df_numerized.corr(method='pearson')

8. Pairing of correlated columns

A sorted linear pairing of the correlated columns

correlated_mat = df_numerized.corr()
corr_pairs = correlation_mat.unstack()
sorted_pairs = corr_pairs.sort_values() # kind="quicksort")

print(sorted_pairs)

#the ones that have a high correlation (> 0.5)

strong_pairs = sorted_pairs[abs(sorted_pairs) > 0.5]

print(strong_pairs)