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app.py

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+import streamlit as st
+import pickle
+import sklearn
+import pandas as pd
+import numpy as np
+from PIL import Image
+model = pickle.load(open('model.sav', 'rb'))
+
+st.title('Player Salary Prediction')
+st.sidebar.header('Player Data')
+image = Image.open('bb.jpg')
+st.image(image, '')
+
+# FUNCTION
+def user_report():
+  rating = st.sidebar.slider('Rating', 50,100, 1 )
+  jersey = st.sidebar.slider('Jersey', 0,100, 1 )
+  team = st.sidebar.slider('Team', 0,30, 1 )
+  position = st.sidebar.slider('Position', 0,10, 1 )
+  country = st.sidebar.slider('Country', 0,3, 1 )
+  draft_year = st.sidebar.slider('Draft Year', 2000,2020, 2000)
+  draft_round = st.sidebar.slider('Draft Round', 1,10, 1)
+  draft_peak = st.sidebar.slider('Draft Peak', 1,30, 1)
+
+
+  user_report_data = {
+      'rating':rating,
+      'jersey':jersey,
+      'team':team,
+      'position':position,
+      'country':country,
+      'draft_year':draft_year,
+      'draft_round':draft_round,
+      'draft_peak':draft_peak
+  }
+  report_data = pd.DataFrame(user_report_data, index=[0])
+  return report_data
+
+user_data = user_report()
+st.header('Player Data')
+st.write(user_data)
+
+salary = model.predict(user_data)
+st.subheader('Player Salary')
+st.subheader('$'+str(np.round(salary[0], 2)))

bb-player-salary-prediction.ipynb

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+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nplt.style.use('dark_background')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = pd.read_csv('../input/nba2k20-player-dataset/nba2k20-full.csv')\ndf.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.shape","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.info()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = df.dropna()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.isnull().sum().sum()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = df.drop(['full_name', 'b_day', 'height', 'weight', 'college'], axis = 1)\ndf.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def removehash(value):\n    value = value[1:]\n    return int(value)\n\ndf['jersey'] = df['jersey'].apply(removehash)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df['salary'] = df['salary'].apply(removehash)\ndf.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df['team'].value_counts()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df['country'].value_counts()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def removecountryoutlier(value):\n    if value not in ['USA', 'Canada', 'Australia']:\n        return 'Others'\n    else:\n        return value\n\ndf['country'] = df['country'].apply(removecountryoutlier)\ndf['country'].value_counts()\n   ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df['position'].value_counts()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df['draft_round'].unique","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df['draft_peak'].unique","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def removeundrafted(value):\n    if value=='Undrafted':\n        return \n    else:\n        return value\n    \ndf['draft_round'] = df['draft_round'].apply(removeundrafted)\ndf['draft_peak'] = df['draft_peak'].apply(removeundrafted)\n\ndf = df.dropna()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\nle = LabelEncoder()\ndf['position'] = le.fit_transform(df['position'])\ndf['country'] = le.fit_transform(df['country'])\ndf['team'] = le.fit_transform(df['team'])\n\ndf.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x = df.drop(['salary'], axis = 1)\ny = df['salary']","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nx_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.linear_model import LinearRegression\nlr = LinearRegression()\nlr.fit(x_train, y_train)\npredictions = lr.predict(x_test)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"diff = y_test - predictions","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import seaborn as sns\nsns.distplot(diff)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pickle\npickle.dump(lr, open('./model.sav', 'wb'))","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}

requirements.txt

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+numpy==1.21.5
+pandas==1.4.4
+Pillow==9.3.0
+scikit_learn==1.2.0
+streamlit==1.16.0