Merge pull request larymak#235 from Vinayak119A/contribute

added cricket score predictor project

Author: larymak

MachineLearning Projects/Cricket Score Predictor/README.md

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+# Cricket score predictor
+
+This is a machine learning based project built with python and a model that's been trained specifically to predict cricket scores of upcoming matches with as much canonicity and
+accuracy possible.
+
+Here's a demonstration snapshot of our project.
+
+![MicrosoftTeams-image](https://user-images.githubusercontent.com/113302353/198577345-949da015-15cb-4dab-9cdc-6ad4a9f0bb3d.png)

MachineLearning Projects/Cricket Score Predictor/app.py

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+import streamlit as st
+import pickle
+import pandas as pd
+import numpy as np
+
+pipe = pickle.load(open('pipe.pkl','rb'))
+
+teams = ['Australia',
+ 'India',
+ 'Bangladesh',
+ 'New Zealand',
+ 'South Africa',
+ 'England',
+ 'West Indies',
+ 'Afghanistan',
+ 'Pakistan',
+ 'Sri Lanka']
+
+cities = ['Colombo',
+ 'Mirpur',
+ 'Johannesburg',
+ 'Dubai',
+ 'Auckland',
+ 'Cape Town',
+ 'London',
+ 'Pallekele',
+ 'Barbados',
+ 'Sydney',
+ 'Melbourne',
+ 'Durban',
+ 'St Lucia',
+ 'Wellington',
+ 'Lauderhill',
+ 'Hamilton',
+ 'Centurion',
+ 'Manchester',
+ 'Abu Dhabi',
+ 'Mumbai',
+ 'Nottingham',
+ 'Southampton',
+ 'Mount Maunganui',
+ 'Chittagong',
+ 'Kolkata',
+ 'Lahore',
+ 'Delhi',
+ 'Nagpur',
+ 'Chandigarh',
+ 'Adelaide',
+ 'Bangalore',
+ 'St Kitts',
+ 'Cardiff',
+ 'Christchurch',
+ 'Trinidad']
+
+st.title('Cricket Score Predictor')
+
+col1, col2 = st.columns(2)
+
+with col1:
+    batting_team = st.selectbox('Select batting team',sorted(teams))
+with col2:
+    bowling_team = st.selectbox('Select bowling team', sorted(teams))
+
+city = st.selectbox('Select city',sorted(cities))
+
+col3,col4,col5 = st.columns(3)
+
+with col3:
+    current_score = st.number_input('Current Score')
+with col4:
+    overs = st.number_input('Overs done(works for over>5)')
+with col5:
+    wickets = st.number_input('Wickets out')
+
+last_five = st.number_input('Runs scored in last 5 overs')
+
+if st.button('Predict Score'):
+    balls_left = 120 - (overs * 6)
+    wickets_left = 10 - wickets
+    crr = current_score / overs
+
+    input_df = pd.DataFrame(
+        {'batting_team': [batting_team], 'bowling_team': [bowling_team], 'city': city, 'current_score': [current_score],
+         'balls_left': [balls_left], 'wickets_left': [wickets], 'crr': [crr], 'last_five': [last_five]})
+    result = pipe.predict(input_df)
+    st.header("Predicted Score - " + str(int(result[0])))