Absolutely! Here’s a guide to help you set up and manage your own AI-based lead scoring system using Python and scikit-learn, which can integrate well with your Odoo CRM:
1. Setting Up the Environment
Make sure you have Python 3.8 and scikit-learn installed. You can use pip to install scikit-learn if you haven’t already:
“`bash
pip install scikit-learn
“`
2. Data Preparation
Your first step will be to prepare your lead data. Let’s assume you have a dataset in a CSV file named `leads.csv` with the following columns:
– `LeadID`
– `CompanySize`
– `Industry`
– `Budget`
– `EngagementScore`
– `LeadSource`
– `Converted` (target variable, 1 for converted, 0 for not converted)
3. Loading and Preprocessing Data
Here’s how you can load and preprocess the data using Python:
“`python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder
Load data
data = pd.read_csv(‘leads.csv’)
Encode categorical variables
label_encoders = {}
categorical_columns = [‘Industry’, ‘LeadSource’]
for col in categorical_columns:
le = LabelEncoder()
data[col] = le.fit_transform(data[col])
label_encoders[col] = le
Split data into features and target
X = data.drop([‘LeadID’, ‘Converted’], axis=1)
y = data[‘Converted’]
Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
Feature scaling
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
“`
4. Training a Machine Learning Model
Let’s train a logistic regression model:
“`python
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
Initialize and train the model
model = LogisticRegression()
model.fit(X_train, y_train)
Make predictions
y_pred = model.predict(X_test)
Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
class_report = classification_report(y_test, y_pred)
print(f”Accuracy: {accuracy}”)
print(f”Confusion Matrix:n{conf_matrix}”)
print(f”Classification Report:n{class_report}”)
“`
5. Integrating with Odoo
To integrate this model with Odoo, follow these steps:
1. Save the Model: Use `joblib` to save the trained model.
“`python
import joblib
Save the model and scaler
joblib.dump(model, ‘lead_scoring_model.pkl’)
joblib.dump(scaler, ‘scaler.pkl’)
“`
2. Load and Use the Model in Odoo: In your Odoo module, you can use the saved model to score new leads.
“`python
import joblib
import pandas as pd
Load the model and scaler
model = joblib.load(‘lead_scoring_model.pkl’)
scaler = joblib.load(‘scaler.pkl’)
Define a function to score leads
def score_leads(leads_df):
Preprocess and scale data
leads_df = leads_df.copy()
for col in categorical_columns:
leads_df[col] = label_encoders[col].transform(leads_df[col])
X_leads = scaler.transform(leads_df)
predictions = model.predict(X_leads)
return predictions
“`
3. Integrate with Odoo’s Lead Model: You can use this function within your custom Odoo model or controller to evaluate and score leads based on the predictions.
6. Automating Lead Scoring
Consider setting up scheduled actions or triggers in Odoo to automatically score leads at certain intervals or upon specific events.
Final Notes
– Model Performance: Continuously monitor and retrain your model with updated data to maintain accuracy.
– Data Security: Ensure that any sensitive data is handled securely.
Feel free to adjust the code based on your specific requirements and data characteristics. If you need more details or have specific questions, just let me know!