predictive analysis

1. 9/20/23, 10:55 PM Untitled19.ipynb - Colaboratory
https://colab.research.google.com/drive/1yp6bmte8FJDIV36MybTtJcHeugjFQKF6#scrollTo=1hbjGmTcn40T&printMode=true 1/5
import pandas as pd
boll = pd.read_csv('/content/bollywood.csv')
SlNo
Release
Date
MovieName ReleaseTime Genre Budget BoxOfficeCollection YoutubeViews YoutubeLi
0 1
18-Apr-
14
2 States LW Romance 36 104.0 8576361 26
1 2
4-Jan-
13
Table No.
21
N Thriller 10 12.0 1087320 1
boll.head(2)
boll.Genre.value_counts()
Comedy 36
Drama 35
Thriller 26
Romance 25
Action 21
Thriller 3
Action 3
Name: Genre, dtype: int64
boll[['Genre','ReleaseTime']].value_counts()
Genre ReleaseTime
Drama N 24
Comedy N 23
Thriller N 20
Romance N 15
Action N 12
Drama HS 6
Comedy LW 5
HS 5
Thriller FS 4
Romance LW 4
Drama FS 4
Comedy FS 3
Action N 3
Romance FS 3
HS 3
Action LW 3
HS 3
FS 3
Thriller N 2
Thriller HS 1
LW 1
Drama LW 1
Thriller LW 1
dtype: int64
boll['month'] = pd.DatetimeIndex(boll['Release Date']).month
boll.head(1)

2. 9/20/23, 10:55 PM Untitled19.ipynb - Colaboratory
https://colab.research.google.com/drive/1yp6bmte8FJDIV36MybTtJcHeugjFQKF6#scrollTo=1hbjGmTcn40T&printMode=true 2/5
SlNo
Release
Date
MovieName ReleaseTime Genre Budget BoxOfficeCollection YoutubeViews YoutubeLi
0 1
18-Apr-
14
2 States LW Romance 36 104.0 8576361 26
boll['month'].value_counts()
1 20
3 19
5 18
7 16
2 16
4 11
9 10
6 10
11 10
10 9
8 8
12 2
Name: month, dtype: int64
boll[boll['Budget']>25][['MovieName','month']].value_counts()
MovieName month
2 States 4 1
Raja Natwarlal 8 1
Kill Dil 11 1
Kochadaiiyaan 5 1
Krrish 3 11 1
..
Highway 2 1
Himmatwala 3 1
Holiday 6 1
Humshakals 6 1
Zilla Ghaziabad 2 1
Length: 62, dtype: int64
boll['ROI']=(boll['BoxOfficeCollection']-boll['Budget'])/boll['Budget']
boll.nlargest(10,['ROI'])

3. 9/20/23, 10:55 PM Untitled19.ipynb - Colaboratory
https://colab.research.google.com/drive/1yp6bmte8FJDIV36MybTtJcHeugjFQKF6#scrollTo=1hbjGmTcn40T&printMode=true 3/5
SlNo
Release
Date
MovieName ReleaseTime Genre Budget BoxOfficeCollection YoutubeViews Youtube
64 65
26-Apr-
13
Aashiqui 2 N Romance 12 110.0 2926673
89 90
19-Dec-
14
PK HS Drama 85 735.0 13270623
132 133
13-Sep-
13
Grand
Masti
LW Comedy 35 298.0 1795640
135 136
20-Sep-
13
The
Lunchbox
N Drama 10 85.0 1064854
87 88
14-Jun-
13
Fukrey N Comedy 5 36.2 227912
58 59
5-Sep-
14
Mary Kom N Drama 15 104.0 6086811
128 129
18-Oct-
13
Shahid FS Drama 6 40.0 1148516
37 38
11-Jul-
14
Humpty
Sharma Ki
Dulhania
N Romance 20 130.0 6604595
101 102
12-Jul-
13
Bhaag
Milkha
Bhaag
N Drama 30 164.0 2635390
115 116
9-Aug-
13
Chennai
Express
FS Comedy 75 395.0 1882346
boll.groupby('ReleaseTime')['ROI'].mean()
ReleaseTime
FS 0.973853
HS 0.850867
LW 1.127205
N 0.657722
Name: ROI, dtype: float64
(array([64., 40., 19., 11., 4., 4., 2., 2., 1., 2.]),
array([ 2. , 16.8, 31.6, 46.4, 61.2, 76. , 90.8, 105.6, 120.4,
135.2, 150. ]),
<BarContainer object of 10 artists>)
import matplotlib.pyplot as plt
import seaborn as sn
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
plt.hist(boll['Budget'])
sn.distplot(boll['Budget'])

4. 9/20/23, 10:55 PM Untitled19.ipynb - Colaboratory
https://colab.research.google.com/drive/1yp6bmte8FJDIV36MybTtJcHeugjFQKF6#scrollTo=1hbjGmTcn40T&printMode=true 4/5
<Axes: xlabel='Budget', ylabel='Density'>
<matplotlib.legend.Legend at 0x7bea32b63880>
sn.distplot(boll[boll['Genre']=='Comedy']['ROI'],color='g',label='comedy')
sn.distplot(boll[boll['Genre']=='Drama']['ROI'],color='r',label='drama')
plt.legend()
BoxOfficeCollection YoutubeLikes
BoxOfficeCollection 1.000000 0.682517
YoutubeLikes 0.682517 1.000000
feature=['BoxOfficeCollection','YoutubeLikes']
boll[feature].corr()
heatfeature=['Budget', 'BoxOfficeCollection','YoutubeViews','YoutubeLikes','YoutubeDislikes']
sn.heatmap(boll[heatfeature].corr(),annot= True)

5. 9/20/23, 10:55 PM Untitled19.ipynb - Colaboratory
https://colab.research.google.com/drive/1yp6bmte8FJDIV36MybTtJcHeugjFQKF6#scrollTo=1hbjGmTcn40T&printMode=true 5/5
<Axes: >