> ## Documentation Index > Fetch the complete documentation index at: https://help.peliqan.io/llms.txt > Use this file to discover all available pages before exploring further. # Lead Conversion > Predict which leads have the most propensity to convert. Predict which leads have the most propensity to convert. We will train a machine learning (ML) model based on historical lead conversion data, in order to predict likelihood of conversion for new leads. Determining the **value of each lead** is a critical factor for businesses that want to **maximize their sales efforts**. It helps them identify which leads are **most likely to convert** and generate revenue over time. Here is an example on how to build an ML model in Peliqan.io with a few lines of Python code. ### Import required modules ```python theme={null} from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score from sklearn.preprocessing import LabelEncoder from collections import defaultdict from joblib import dump import pandas as pd ``` ### Load a dataset Load data from a table into a dataframe (df). The table needs to contains lead data, including an indication if these leads converted (historical data). ```python theme={null} # Load Data dbconn = pq.dbconnect(pq.DW_NAME) df = dbconn.fetch(pq.DW_NAME, 'crm', 'leads', df=True) ``` ### Using Streamlit to build an app We use the [Streamlit](https://docs.streamlit.io/library/api-reference/text) module (st), built into Peliqan.io, to build a UI and show data. ```python theme={null} # Show a title (st = Streamlit module) st.title("Lead Conversion") # Show some text st.text("Predict Hot Leads") # Show the dataframe st.dataframe(df.head(), use_container_width=True) ``` This is what the output looks like: image Here’s our code in the Peliqan low-code editor, with a preview: image ### Prepare the data We remove unwanted columns (features) from our leads and convert categories (e.g. lead source) to a numerical value: ```python theme={null} # Drop unwanted features drop_features = ['Prospect ID', 'index', 'Prediction'] df = df.drop(drop_features, axis=1) encoder = defaultdict(LabelEncoder) # Apply Label Encoding to convert categorical variables to Numerical cat_cols = df.select_dtypes('object').columns df[cat_cols] = df[cat_cols].apply(lambda x: encoder[x.name].fit_transform(x)) # Save the label encoder for future predictions dump(encoder, '/data_app/encoder_lead_conversion') ``` ### Train and save the model Once the data is ready we split it into a training set and a testing sets to evaluate the model. We save the model to make more predictions later on. ```python theme={null} # Training data to train model on X = df.drop('Converted', axis=1) # Feature to predict Y = df['Converted'] # Split data into train test and train model with .fit() X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.30, random_state=0) model = LogisticRegression().fit(X_train,Y_train) # Make prediction pred = model.predict(X_test) # Evaluate the model accuracy = accuracy_score(pred, Y_test) st.text("Accuracy: " + str(accuracy)) # Save the model for future real-time predictions dump(model, '/data_app/model_lead_conversion') st.success('Model saved successfully!') ``` Expand this to see the full code ```python theme={null} from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score from sklearn.preprocessing import LabelEncoder from collections import defaultdict from joblib import dump import pandas as pd # Load Data dbconn = pq.dbconnect('dw_123') df = dbconn.fetch('dw_123', 'crm', 'leads', df=True) # Show a title (st = Streamlit module) st.title("Lead Conversion - predict hot leads") # Show some text st.text("Predict Hot Leads") # Show the dataframe st.dataframe(df.head(), use_container_width=True) # Drop unwanted features drop_features = ['Prospect ID', 'index', 'Prediction'] df = df.drop(drop_features, axis=1) encoder = defaultdict(LabelEncoder) # Apply Label Encoding to convert categorical variables to Numerical cat_cols = df.select_dtypes('object').columns df[cat_cols] = df[cat_cols].apply(lambda x: encoder[x.name].fit_transform(x)) # Save the label encoder for future predictions dump(encoder, '/data_app/encoder_lead_conversion') # Training data to train model on X = df.drop('Converted', axis=1) # Feature to predict Y = df['Converted'] # Split data into train test and train model with .fit() X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.30, random_state=0) model = LogisticRegression().fit(X_train,Y_train) # Make prediction pred = model.predict(X_test) # Evaluate model accuracy = accuracy_score(pred, Y_test) st.text("Accuracy: " + str(accuracy)) # Save the model for future real-time predictions dump(model, '/data_app/model_lead_conversion') st.success('Model saved successfully') ``` ## Next Steps 1. You can make **predictions on real-time incoming data** using the saved model. Learn more about [making real-time predictions on new incoming data.](/low-code-python-data-apps/predictions-machine-learning-templates/write-predictions-to-a-table) 2. You can make real-time predictions on new incoming data and send [**alerts to slack**](/low-code-python-data-apps/predictions-machine-learning-templates/send-alerts-to-slack) if the model makes a prediction **above a certain threshold**. 3. Using **Peliqan** you can **create an app for business users to consume** the model you have made. Learn more about [creating apps for users to consume your ML model](/low-code-python-data-apps/predictions-machine-learning-templates/create-an-app-for-business-users-to-get-predictions)**.** This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.