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autoanalysis/Automate_Analysis/AutoML/Procfile

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+web: sh setup.sh && streamlit run newml.py

autoanalysis/Automate_Analysis/AutoML/README.md

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+# AutoML - For HR Domain
+Build AutoML application using Streamlit

autoanalysis/Automate_Analysis/AutoML/activities/activity.py

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+# Importing libraries.
+import time
+import streamlit as st
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import cross_val_score
+from sklearn.metrics import accuracy_score, confusion_matrix
+from ml.mlmodel import MLModels
+
+
+
+def eda(df):
+    '''
+    Description: 
+            Method that provides various EDA options.
+            
+    Parameters: 
+            df - A pandas dataframe.
+    
+    Returns: 
+           Nothing. 
+    '''
+    rows, columns = df.shape[0], df.shape[1]
+    st.info(f'Rows = {rows}, Columns = {columns}')
+    if st.checkbox('Show Target Classes and Value Counts'):
+        target_classes = df.target.value_counts()
+        st.dataframe(target_classes)
+    if st.checkbox("Show DataFrame"):
+        num_rows = st.number_input(label="Enter number of rows", min_value=5, max_value=rows)
+        st.dataframe(df.head(num_rows))
+    if st.checkbox("Describe The Data"):
+        st.dataframe(df.describe())
+    if st.checkbox("Show DataFrame By Specific Columns"):
+        column_names = st.multiselect("Select Columns", df.columns)
+        st.dataframe(df[column_names])
+    if st.checkbox("Show Data Types"):
+        st.dataframe(df.dtypes)
+
+def vis(df):
+    '''
+    Description: 
+            Method for various visualization options.
+            
+    Parameters: 
+            df - A pandas dataframe.
+    
+    Returns: 
+           Nothing. 
+    '''
+    if st.button("Correlational Matrix"):
+        with st.spinner('Generating A Correlational Matrix...'):
+            time.sleep(3)
+        sns.heatmap(df.corr(), annot=True)
+        st.pyplot()
+    if st.button("Value Counts"):
+        with st.spinner('Generating A Value Count Plot...'):
+            time.sleep(3)
+        df.target.value_counts().plot(kind='barh')
+        st.pyplot()
+    if st.button("Pair Plot"):
+        with st.spinner('Generating A Pair Plot...'):
+            time.sleep(3)
+        sns.pairplot(df, hue='target')
+        st.pyplot()
+    if st.button("Pie Chart"):
+        with st.spinner('Generating A Pie Chart...'):
+            time.sleep(3)
+        df.target.value_counts().plot.pie(autopct='%1.2f%%')
+        st.pyplot()
+    if st.checkbox('Scatter Plot'):
+        x_val = st.selectbox('Select a column for x-axis', df.columns)
+        y_val = st.selectbox('Select a column for y-axis', df.columns)
+        with st.spinner('Generating A Scatter Plot...'):
+            time.sleep(3) 
+        plt.scatter(df[x_val], df[y_val], c=df.target)
+        plt.xlabel(x_val)
+        plt.ylabel(y_val)
+        st.pyplot()
+
+def ml(df):
+    '''
+    Description: 
+            Method for handling all the machine learning options.
+            
+    Parameters: 
+            df - A pandas dataframe.
+    
+    Returns: 
+           Nothing. 
+    '''
+    def run_ml_model(model_name):
+        '''
+        Description: 
+                An inner method for running a machine learning model.
+            
+        Parameters: 
+                model_name - A machine learning model name as a string.
+    
+        Returns: 
+                Nothing.
+        '''
+        if model_name == 'Linear Regression':
+           
+            lin_reg = clf.linear_regression()
+            lin_reg.fit(x_train, y_train)
+            coeff = lin_reg.coef_
+            intercept = lin_reg.intercept_
+            st.success(f'The coefficients = {coeff}')
+            st.success(f'The intercept = {intercept}')
+            st.write('Now make an equation of the form y = a1*x1 + a2*x2 + ... an*xn + c')
+            st.write('and plugin the features and compare the value you get with the actual target value.')
+            st.info('NOTE: Linear Regression is not for classification problems. Hence, use it for Boston Houses or Diabetes dataset to understand this algorithm deeply.')
+        elif model_name == 'Logistic Regression':
+           
+            C = st.slider(label='Choose C', min_value=0.1, max_value=5.0)
+            log_reg = clf.logistic_regression(C)
+            train_and_display_metrics(log_reg)
+            if st.checkbox('KFold Cross Validation'):
+                run_kfold(log_reg) 
+        elif model_name == 'K Nearest Neighbors':
+
+            n_neighbors = st.number_input(label='n_neighbors', min_value=5, max_value=100)
+            knn = clf.k_nearest_neighbors(n_neighbors)
+            train_and_display_metrics(knn)
+            if st.checkbox('KFold Cross Validation'):
+                run_kfold(knn)
+            st.info('NOTE: It is often a good practice to scale the features when using KNN because it uses Eucledian distances. However, this topic comes under feature engineering (intermediate level).')
+        elif model_name == 'Naive Bayes (Gaussian)':
+            
+            nbg = clf.naive_bayes()
+            train_and_display_metrics(nbg)
+            if st.checkbox('KFold Cross Validation'):
+                run_kfold(nbg)
+        elif model_name == 'SVM':
+           
+            C = st.slider(label='Choose C', min_value=0.1, max_value=5.0)
+            kernel = st.selectbox('Kernel', ['rbf', 'poly', 'linear'])
+            svm = clf.svm(C, kernel)
+            train_and_display_metrics(svm)
+            if st.checkbox('KFold Cross Validation'):
+                run_kfold(svm) 
+        elif model_name == 'Decision Tree':
+            
+            max_depth = st.number_input(label='max_depth', min_value=10, max_value=100)
+            dt = clf.decision_tree(max_depth)
+            train_and_display_metrics(dt)
+            if st.checkbox('KFold Cross Validation'):
+                run_kfold(dt) 
+        elif model_name == 'Random Forest':
+            
+            n_estimators = st.number_input('n_estimators', min_value=100, max_value=1000)
+            max_depth = st.number_input(label='max_depth', min_value=10, max_value=100)
+            rf = clf.random_forest(n_estimators, max_depth)
+            train_and_display_metrics(rf)
+            if st.checkbox('KFold Cross Validation'):
+                run_kfold(rf) 
+
+    def train_and_display_metrics(model):
+        '''
+        Description: 
+                Method to train the model and display its accuracy.
+            
+        Parameters: 
+                model - A ML model (from sklearn).
+    
+        Returns: 
+                Nothing.
+        '''
+        model.fit(x_train, y_train)
+        y_pred_test = model.predict(x_test)
+        y_pred_train = model.predict(x_train)
+        st.success(f'Train accuracy = {accuracy_score(y_train, y_pred_train)*100:.5f}%')
+        st.success(f'Test accuracy = {accuracy_score(y_test, y_pred_test)*100:.5f}%')
+        if st.button('Show Confusion Matrix'):
+            cf_matrix = confusion_matrix(y_test, y_pred_test)
+            sns.heatmap(cf_matrix, annot=True)
+            st.pyplot()
+
+    def run_kfold(model):
+        '''
+        Description: 
+                Method for running kfold cross validation.
+            
+        Parameters: 
+                model - A ML model (from sklearn).
+    
+        Returns: 
+                Nothing.
+        '''
+        cv = st.number_input(label='Choose number of folds', min_value=5, max_value=20)
+        cv_score = cross_val_score(model,x,y, cv=cv)
+        sum = 0
+        for s in cv_score:
+            sum += s
+        
+        avg_score = sum/cv 
+        st.write(f'According to {cv} kfolds, the following test accuracies have been recorded:')
+        st.dataframe(cv_score)
+        st.success(f'Average test accuracy = {avg_score*100:.5f}%')
+
+    clf = MLModels()
+    x = df.iloc[:, :-1]
+    y = df.iloc[:, -1]
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+
+    model_name = st.selectbox("Choose a model/algorithm", ["Linear Regression", "Logistic Regression", "K Nearest Neighbors", "Naive Bayes (Gaussian)", "SVM", "Decision Tree", "Random Forest"])
+    run_ml_model(model_name)
+   

autoanalysis/Automate_Analysis/AutoML/app.py

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+import streamlit as st
+import streamlit.components.v1 as components
+from activities.activity import ml
+from ml.mlmodel import MLDataset
+import plotly.express as px
+import pandas as pd
+import pandas_profiling
+import os
+from sklearn.datasets import load_diabetes
+from streamlit_pandas_profiling import st_profile_report
+from streamlit_option_menu import option_menu
+
+
+def main():
+    
+     #Sidebar
+    from PIL import Image
+    st.sidebar.image('logo.png', use_column_width=True)
+    image_loan=Image.open(os.path.join("data analysis.jpg"))
+    rad = st.sidebar.radio("Navigation",["Home","Analysis","Visualize","Machine-Learning"])
+    # if rad=="Home":
+    #     HtmlFile = open("style.css", 'r', encoding='utf-8')
+    #     source_code = HtmlFile.read() 
+    #     components.html(source_code,width=900, height=700)
+    #     # print(source_code)
+
+    with open('style.css') as f:
+        st.markdown(f'<style>{f.read()}</stle>',unsafe_allow_html=True)
+    if rad=='Home':
+        html_temp = """
+        <div          id="container">
+        <h1>Welcome to Our Website</h1>
+        <p>Our advanced software will help you make sense<br>
+         of your data quickly and easily. With powerful <br>
+         algorithms and customizable dashboards, you'll<br>
+         be able to see patterns and insights that<br>
+         you never knew existed.</p>
+         
+        </div>
+        """
+        st.markdown(html_temp,unsafe_allow_html=True)
+    if rad == "Visualize":
+        file_upload=st.sidebar.file_uploader(" ",type=["csv"])
+        st.sidebar.image(image_loan,use_column_width=True) 
+        chart_select = st.sidebar.selectbox(
+            label = "select the chart type",
+            options=['ScatterPlots','Lineplots','Histogram','Boxplot']
+        )
+        html_temp = """
+        <div style="background-color:red;padding:10px">
+        <h2 style="color:white;text-align:center;border-radius:30px">Automatic Exploratory Analysis</h2>
+        </div>
+        """
+        st.markdown(html_temp,unsafe_allow_html=True)
+        st.sidebar.title("Upload Input csv file : ")
+        st.subheader('1.Datasets')
+        st.write("""
+    # Automatic Exploratory Analysis
+    """)
+
+
+        if file_upload is not None:
+            df = pd.read_csv(file_upload)
+            st.write(df)
+            numeric_columns = list(df.select_dtypes(['float','int']).columns)
+            if chart_select == "ScatterPlots":
+                st.sidebar.subheader("ScatterPlot Settings")
+                x_values = st.sidebar.selectbox('X axis',options=numeric_columns)
+                y_values = st.sidebar.selectbox('Y axis',options=numeric_columns)
+                plot = px.scatter(data_frame =df, x=x_values,y=y_values)
+                st.plotly_chart(plot)
+            if chart_select == "Lineplots":
+                st.sidebar.subheader("ScatterPlot Settings")
+                x_values = st.sidebar.selectbox('X axis',options=numeric_columns)
+                y_values = st.sidebar.selectbox('Y axis',options=numeric_columns)
+                plot = px.line(data_frame =df, x=x_values,y=y_values)
+                st.plotly_chart(plot)
+            if chart_select=="Histogram":
+                st.sidebar.subheader("ScatterPlot Settings")
+                x_values = st.sidebar.selectbox('X axis',options=numeric_columns)
+                y_values = st.sidebar.selectbox('Y axis',options=numeric_columns)
+                plot=px.histogram(data_frame=df,x=x_values,y=y_values)
+                st.plotly_chart(plot)
+
+            if chart_select=="Boxplot":
+                st.sidebar.subheader("ScatterPlot Settings")
+                x_values = st.sidebar.selectbox('X axis',options=numeric_columns)
+                y_values = st.sidebar.selectbox('Y axis',options=numeric_columns)
+                plot=px.box(data_frame=df,x=x_values,y=y_values)
+                st.plotly_chart(plot)
+            
+        else:
+            st.info('Awaiting for CSV file to be uploaded.')
+    if rad == "Analysis":
+         
+        file_upload=st.sidebar.file_uploader(" ",type=["csv"])
+        st.sidebar.image(image_loan,use_column_width=True) 
+        html_temp = """
+        <div style="background-color:red;padding:10px">
+        <h2 style="color:white;text-align:center;">Automatic Exploratory Analysis </h2>
+        </div>
+        """
+        st.markdown(html_temp,unsafe_allow_html=True)
+        st.sidebar.title("Upload Input csv file : ")
+        st.subheader('1.Datasets')
+        st.write("""
+    # Automated Exploratory Analysis
+    """)
+
+
+        if file_upload is not None:
+            df = pd.read_csv(file_upload)
+            st.write(df)
+            df1 = df.dropna()
+            if st.button('Run Modeling'):
+                st.title("Exploratory Data Analysis")
+                profile_df = df.profile_report()
+                st_profile_report(profile_df)
+            if st.button("No. of Missing values"):
+                    st.write(df.isna().sum())
+            if st.button('Drop Missing values'):
+                df1 = df.dropna()
+                st.write(df1)
+ 
+        else:
+            st.info('Awaiting for CSV file to be uploaded.')
+            if st.button('Press to use Example Datasets'):
+                diabetes = load_diabetes()
+                X = pd.DataFrame(diabetes.data,columns=diabetes.feature_names)
+                Y = pd.Series(diabetes.target,name='response')
+                df = pd.concat([X,Y],axis=1)
+                st.markdown('The Diabetes dataset is used as the example.')
+                st.write(df.head())
+                st.markdown("Shape of Diabetes dataset")
+                st.write(df.shape)
+                st.title("Exploratory Data Analysis")
+                profile_df = df.profile_report()
+                st_profile_report(profile_df)
+                st.title("Sum of Null Values")
+                st.write(df.isna().sum())
+                st.title("Dropping Null Values")
+                df1 = df.dropna()
+                data = df1.to_csv("new.csv")
+                st.write(df1)
+    if rad == "Machine-Learning":
+            file_upload=st.sidebar.file_uploader(" ",type=["csv"])
+            dataset_name = st.selectbox("Pick a dataset", ["Iris", "Breast Cancer", "Wine Quality", "Mnist Digits", "Boston Houses", 'Diabetes'])
+            dataset = MLDataset(dataset_name)
+            df = dataset.get_dataframe()  
+
+            ml(df)
+            
+            
+                
+                
+                
+            
+                
+
+                
+                
+
+                
+              
+                        
+                    
+                    
+                        
+                            
+
+
+
+if __name__ == '__main__':
+    main()

autoanalysis/Automate_Analysis/AutoML/homePage.html

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+<!DOCTYPE html>
+<html>
+  <head>
+    <meta charset="UTF-8">
+    <title>Welcome Page</title>
+    <style>
+      /* CSS styles for the page */
+      body {
+        background-color: #F7F7F7;
+        font-family: Arial, sans-serif;
+      }
+
+      #container {
+        max-width: 80%;
+        max-height: 60%;
+        margin-top: 5rem;
+        padding: 40px;
+        box-shadow:12px 20px 5px rgb(13 14 10 / 10%);
+        border: 3px solid rgb(57, 154, 193);
+        
+        /* transition: transform 0.3s ease; */
+        background: linear-gradient(114.96deg,#823ddc 34.12%,rgb(12, 230, 158) 105.4%);
+      }
+
+       #image:hover {
+        transform: translateY(-20px);
+      } 
+
+      h1 {
+        font-size: 3rem;
+        text-align: center;
+        margin-bottom: 20px;
+        margin-left: -50rem;
+        color: #333333;
+        text-shadow: 1px 1px 2px rgba(0, 0, 0, 0.1);
+      }
+
+      p {
+        font-size: 1.7rem;
+        font-family: cursive;
+        text-align: center;
+        margin-left: -47rem;
+        color: white;
+        text-shadow: 1px 1px 2px rgba(0, 0, 0, 0.1);
+      }
+      ::-webkit-scrollbar{
+        display: none;
+      }
+
+      button {
+        display: block;
+        margin: 0 auto;
+        font-size: 1.2rem;
+        margin-top: 4rem;
+        padding: 10px 20px;
+        margin-left: 5rem;
+        background-color:rgb(222, 131, 20);
+        color: #FFFFFF;
+        border: none;
+        border-radius: 5rem;
+        cursor: pointer;
+        transition: background-color 0.3s ease;
+      }
+
+      button:hover {
+        background-color:rgb(245, 199, 113);
+      }
+      .analysis{
+            margin-left: 60rem;
+            margin-top: -22rem; 
+            border-radius: 5px;
+            z-index: +1;
+            box-shadow: 7px 7px 2px rgba(0, 0, 0, 0.1);
+        } 
+      .data{
+            margin-left: 50rem;
+            /* margin-bottom: rem; */
+            margin-top: -5rem;
+            border-radius: .7rem;
+            box-shadow: 7px 7px 2px rgba(0, 0, 0, 0.1);
+        }
+    </style>
+  </head>
+  <body>
+    <div id="container">
+      <h1>Welcome to Our Website</h1>
+      <p>Our advanced software will help you make sense of<br>
+         your data quickly and easily. With powerful <br>
+         algorithms and customizable dashboards, you'll<br>
+         be able to see patterns and insights that<br>
+         you never knew existed.</p>
+      <button onclick="location.href='#'">Get Started</button>
+
+
+    </div>
+<!-- 
+    <script>
+      // JavaScript for the page
+      console.log('Welcome to our website!');
+
+      // Animate the container on scroll
+      const container = document.querySelector('#container');
+      window.addEventListener('scroll', () => {
+        const containerTop = container.getBoundingClientRect().top;
+        const containerBottom = container.getBoundingClientRect().bottom;
+        const viewportHeight = window.innerHeight;
+        if (containerTop < viewportHeight && containerBottom > 0) {
+          container.style.opacity = 1;
+          container.style.transform = 'translateY(0)';
+        } else {
+          container.style.opacity = 0;
+          container.style.transform = 'translateY(20px)';
+        }
+      });
+    </script> -->
+  </body>
+</html>

autoanalysis/Automate_Analysis/AutoML/logs.log

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+2023-04-01 22:42:34,355:WARNING:/Users/useradmin/opt/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5
+  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
+
+2023-04-01 22:45:24,159:WARNING:/Users/useradmin/opt/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5
+  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
+
+2023-04-01 22:50:28,947:WARNING:/Users/useradmin/opt/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5
+  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
+
+2023-04-01 22:52:12,575:WARNING:/Users/useradmin/opt/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5
+  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
+

autoanalysis/Automate_Analysis/AutoML/ml/mlmodel.py

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+# Importing libraries.
+import pandas as pd
+from sklearn.datasets import load_iris, load_breast_cancer, load_wine, load_digits, load_boston, load_diabetes
+from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.svm import SVC
+from sklearn.ensemble import RandomForestClassifier
+
+# Defining ML models class.
+class MLModels:
+    def linear_regression(self):
+        '''
+        Description: 
+                Method for creating a linear regression classifier.
+             
+        Parameters: 
+                None
+    
+        Returns: 
+                clf - A linear regression classifier.  
+        '''
+        clf = LinearRegression()
+        return clf
+
+    def logistic_regression(self, C):
+        '''
+        Description: 
+                Method for creating a logistic regression classifier.
+            
+        Parameters: 
+                C - Inverse of regularization strength.
+    
+        Returns: 
+                clf - A logistic regression classifier.
+        '''
+        clf = LogisticRegression(C=C)
+        return clf
+
+    def k_nearest_neighbors(self, n_neighbors):
+        '''
+        Description: 
+                Method for creating a knn classifier.
+            
+        Parameters: 
+                n_neighbors - Number of neighbors to use.
+    
+        Returns: 
+                clf - A knn classifier.
+        '''
+        clf = KNeighborsClassifier(n_neighbors=n_neighbors)
+        return clf
+
+    def naive_bayes(self):
+        '''
+        Description: 
+                Method for creating a naive-bayes classifier.
+            
+        Parameters: 
+                None
+    
+        Returns: 
+                clf - A naive-bayes classifier.
+        '''
+        clf = GaussianNB()
+        return clf
+
+    def svm(self, C, kernel):
+        '''
+        Description: 
+                Method for creating a svm classifier.
+            
+        Parameters: 
+                C - Regularization parameter.
+                kernel - Specifies the kernel type to be used in the algorithm.
+    
+        Returns: 
+                clf - A svm classifier.
+        '''
+        clf = SVC(C=C, kernel=kernel)
+        return clf
+
+    def decision_tree(self, max_depth):
+        '''
+        Description: 
+                Method for creating a decision tree classifier.
+            
+        Parameters: 
+                max_depth - The maximum depth of the tree.
+    
+        Returns: 
+                clf - A decision tree classifier.
+        '''
+        clf = DecisionTreeClassifier(max_depth=max_depth)
+        return clf
+
+    def random_forest(self, n_estimators, max_depth):
+        '''
+        Description: 
+                Method for creating a decision tree classifier.
+            
+        Parameters: 
+                n_estimatos - The number of trees in the forest.
+                max_depth - The maximum depth of the tree.
+    
+        Returns: 
+                clf - A random forest classifier.
+        '''
+        clf = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth)
+        return clf
+
+    
+# Defining dataset class.
+class MLDataset:
+    def __init__(self, dataset_name=None):
+        '''
+        Description: 
+                Method that initializes the dataset name.
+            
+        Parameters: 
+                dataset_name - The name of the dataset.
+    
+        Returns: 
+                Nothing
+        '''
+        self.dataset_name = dataset_name
+    
+    def get_dataframe(self):
+        '''
+        Description: 
+                Method to get a pandas dataframe based on the dataset name initialized in __init__().
+            
+        Parameters: 
+                None
+    
+        Returns: 
+                df - A pandas dataframe.
+        '''
+        df = None
+        if self.dataset_name == "Iris":
+            iris = load_iris()
+            df = pd.DataFrame(iris.data, columns=iris.feature_names)
+            df['target'] = iris.target
+        elif self.dataset_name == "Breast Cancer":
+            bc = load_breast_cancer()
+            df = pd.DataFrame(bc.data, columns=bc.feature_names)
+            df['target'] = bc.target    
+        elif self.dataset_name == 'Wine Quality':
+            wq = load_wine()
+            df = pd.DataFrame(wq.data, columns=wq.feature_names)
+            df['target'] = wq.target
+        elif self.dataset_name == 'Mnist Digits':
+            dig = load_digits()
+            df = pd.DataFrame(dig.data, columns=dig.feature_names)
+            df['target'] = dig.target
+        elif self.dataset_name == 'Boston Houses':
+            bh = load_boston()
+            df = pd.DataFrame(bh.data, columns=bh.feature_names)
+            df['target'] = bh.target
+        elif self.dataset_name == 'Diabetes':
+            db = load_diabetes()
+            df = pd.DataFrame(db.data, columns=db.feature_names)
+            df['target'] = db.target
+        return df   

autoanalysis/Automate_Analysis/AutoML/new.csv

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autoanalysis/Automate_Analysis/AutoML/newml.py

@@ -0,0 +1,135 @@
+import pandas as pd
+#import pickle
+import streamlit as st 
+import base64
+
+#@st.cache
+def main():
+  try:
+   
+    #st.title("Personal Loan Authenticator")
+    html_temp = """
+    <div style="background-color:tomato;padding:10px">
+    <h2 style="color:white;text-align:center;">Automatic Machine Learning </h2>
+    </div>
+    """
+    st.markdown(html_temp,unsafe_allow_html=True)
+    from PIL import Image
+    image_loan=Image.open("ml4.jpg")
+    st.sidebar.title("Upload Input csv file : ")
+    file_upload=st.sidebar.file_uploader(" ",type=["csv"])
+    st.sidebar.image(image_loan,use_column_width=True) 
+
+    
+    if file_upload is not None:
+            f1=pd.read_csv(file_upload)
+            f1.isna().sum()
+            f1=f1.dropna()
+            X=f1[['age','previous_year_rating','length_of_service','KPI_Met','awards_won']]
+            y=f1['is_promoted']
+            d2=f1[['age','previous_year_rating','length_of_service','KPI_Met','awards_won','is_promoted']]
+            st.text(" ")
+            if st.checkbox('Show Input Data'):
+                st.write(d2)
+            st.subheader("Pick Your Algorithm") 
+            choose_model=st.selectbox(label=' ',options=[' ','Random Forest','Logistic Regression'])
+            if (choose_model=='Random Forest'):
+                from sklearn.model_selection import train_test_split
+                X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0)
+                from sklearn.ensemble import RandomForestClassifier
+                classifier=RandomForestClassifier()
+                classifier.fit(X_train, y_train)
+                y_pred=classifier.predict(X_test)
+                from sklearn.metrics import accuracy_score
+                score=accuracy_score(y_test,y_pred)
+                from sklearn.metrics import confusion_matrix
+                c1=confusion_matrix(y_test,y_pred)
+                st.write("Model Accuracy : ", score)
+                st.write("Confusion Matrix : ", c1)
+                from sklearn.model_selection import cross_val_score
+                cv=cross_val_score(classifier,X_train, y_train,cv=5,scoring='accuracy')
+                st.write("Cross Calidation of Model : ", cv)
+                import matplotlib.pyplot as plt
+                from sklearn import metrics
+                y_pred_proba = classifier.predict_proba(X_test)[::,1]
+                fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)
+                auc = metrics.roc_auc_score(y_test, y_pred_proba)
+                plt.plot(fpr,tpr,label="data 1, auc="+str(auc))
+                plt.legend(loc=4)
+                st.subheader("ROC Curve")
+                st.pyplot(plt)
+                st.subheader("Upload csv file for Predictions : ")
+                file_upload=st.file_uploader("  ",type=["csv"])
+                if file_upload is not None:
+                    data=pd.read_csv(file_upload)
+                    data1=data.dropna()
+                    data=data1[['age','previous_year_rating','length_of_service','KPI_Met','awards_won']]
+                    predictions=classifier.predict(data)
+                    data['employee_id'] = data1['employee_id']
+                    data['Prediction'] = predictions
+                    st.subheader("Find the Predicted Results below :")
+                    st.write(data)
+                    st.text("0 : Not Eligible for Promotion")
+                    st.text("1 : Eligible for Promotion")
+                    csv = data.to_csv(index=False)
+                    b64 = base64.b64encode(csv.encode()).decode()  # some strings <-> bytes conversions necessary here
+                    href = f'<a href="data:file/csv;base64,{b64}">Download The Prediction Results CSV File</a> (right-click and save as &lt;some_name&gt;.csv)'
+                    st.markdown(href, unsafe_allow_html=True)
+                    display_df = st.checkbox(label='Visualize the Predicted Value')
+                    if display_df:
+                        st.bar_chart(data['Prediction'].value_counts())
+                        st.text(data['Prediction'].value_counts())  
+            if (choose_model=='Logistic Regression'):
+                from sklearn.model_selection import train_test_split
+                X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0)
+                from sklearn.linear_model import LogisticRegression
+                classifier = LogisticRegression()
+                classifier.fit(X_train, y_train)
+                y_pred=classifier.predict(X_test)
+                from sklearn.metrics import accuracy_score
+                score=accuracy_score(y_test,y_pred)
+                from sklearn.metrics import confusion_matrix
+                c1=confusion_matrix(y_test,y_pred)
+                st.write("Model Accuracy : ", score)
+                st.write("Confusion Matrix : ", c1)
+                
+                from sklearn.model_selection import cross_val_score
+                cv=cross_val_score(classifier,X_train, y_train,cv=5,scoring='accuracy')
+                st.write("Cross Calidation of Model : ", cv)
+                import matplotlib.pyplot as plt
+                from sklearn import metrics
+                y_pred_proba = classifier.predict_proba(X_test)[::,1]
+                fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)
+                auc = metrics.roc_auc_score(y_test, y_pred_proba)
+                plt.plot(fpr,tpr,label="data 1, auc="+str(auc))
+                plt.legend(loc=4)
+                st.subheader("ROC Curve")
+                st.pyplot(plt)
+                st.subheader("Upload csv file for Predictions : ")
+                file_upload=st.file_uploader("  ",type=["csv"])                             
+                if file_upload is not None:
+                    data=pd.read_csv(file_upload)
+                    data1=data.dropna()
+                    data=data1[['age','previous_year_rating','length_of_service','KPI_Met','awards_won']]
+                    predictions=classifier.predict(data)
+                    data['employee_id'] = data1['employee_id']
+                    data['Prediction'] = predictions
+                    st.subheader("Find the Predicted Results below :")
+                    st.write(data)
+                    st.text("0 : Not Eligible for Promotion")
+                    st.text("1 : Eligible for Promotion")
+                    csv = data.to_csv(index=False)
+                    b64 = base64.b64encode(csv.encode()).decode()  # some strings <-> bytes conversions necessary here
+                    href = f'<a href="data:file/csv;base64,{b64}">Download The Prediction Results CSV File</a> (right-click and save as &lt;some_name&gt;.csv)'
+                    st.markdown(href, unsafe_allow_html=True)
+                    display_df = st.checkbox(label='Visualize the Predicted Value')
+                    if display_df:
+                        st.bar_chart(data['Prediction'].value_counts())
+                        st.text(data['Prediction'].value_counts())  
+                        
+  except:
+    st.header("An Error occurred")           
+                      
+if __name__=='__main__':
+    main()
+    

autoanalysis/Automate_Analysis/AutoML/requirements.txt

@@ -0,0 +1,10 @@
+numpy>=1.9.2
+scipy>=0.15.1
+scikit-learn==0.24.2
+matplotlib>=1.4.3
+pandas>=0.19
+plotly
+pandas_profiling
+streamlit_pandas_profiling
+streamlit_option_menu
+streamlit==1.0.0

autoanalysis/Automate_Analysis/AutoML/setup.sh

@@ -0,0 +1,11 @@
+mkdir -p ~/.streamlit/
+echo "\
+[general]\n\
+email = \"your-email@domain.com\"\n\
+" > ~/.streamlit/credentials.toml
+echo "\
+[server]\n\
+headless = true\n\
+enableCORS=false\n\
+port = $PORT\n\
+" > ~/.streamlit/config.toml

autoanalysis/Automate_Analysis/AutoML/style.css

@@ -0,0 +1,34 @@
+.css-ffhzg2 {
+    position: absolute;
+    color: rgb(250, 250, 250);
+    inset: 0px;
+    overflow: hidden;
+    background: linear-gradient(114.96deg,#823ddc 34.12%,rgb(12, 230, 158) 105.4%);
+}
+#container {
+
+    max-height:150%;
+    margin-top: 5rem;
+    padding: 40px;
+    box-shadow:12px 20px 5px rgb(13 14 10 / 10%);
+    border: 3px solid rgb(57, 154, 193);
+    
+    /* transition: transform 0.3s ease; */
+    
+  }
+
+.st-bb{
+    opacity: 1;
+    background: black;
+}
+
+
+span{
+    scroll-margin-top: 2rem;
+    color: antiquewhite;
+    font-family: cursive;
+}
+p{
+    font-family:'Lucida Sans', 'Lucida Sans Regular', 'Lucida Grande', 'Lucida Sans Unicode', Verdana, sans-serif;
+    font-size:1.6rem;
+}