Created a small skeleton on how the EVP might be interacted with. Add…

…ed some dummy data for example purposes. #22

Signed-off-by: Felix Zailskas <[email protected]>

Pipfile

@@ -9,6 +9,8 @@ name = "pypi"
 [dev-packages]
 
 [packages]
+numpy = "==1.26.1"
+scikit-learn = "==1.3.2"
 
 [requires]
 python_version = "3.10"

src/database/__init__.py

@@ -0,0 +1,13 @@
+# SPDX-License-Identifier: MIT
+# SPDX-FileCopyrightText: 2023 Felix Zailskas <[email protected]>
+
+from .database_dummy import DatabaseDummy
+
+_database = None
+
+
+def get_database() -> DatabaseDummy:
+    global _database
+    if _database is None:
+        _database = DatabaseDummy()
+    return _database

src/database/database_dummy.py

@@ -0,0 +1,17 @@
+# SPDX-License-Identifier: MIT
+# SPDX-FileCopyrightText: 2023 Felix Zailskas <[email protected]>
+
+import json
+
+
+class DatabaseDummy:
+    def __init__(self) -> None:
+        with open("src/database/dummy_leads.json") as f:
+            json_data = json.load(f)["training_leads"]
+            self.data = {d["lead_id"]: d for d in json_data}
+
+    def get_entry_by_id(self, id_: int) -> dict:
+        return self.data[id_]
+
+    def get_all_entries(self):
+        return self.data

src/database/dummy_leads.json

@@ -0,0 +1,59 @@
+{
+  "training_leads": [
+    {
+      "lead_id": 0,
+      "company_name": "test_company",
+      "first_name": "test_first",
+      "last_name": "test_last",
+      "country_code": "DE",
+      "phone_number": 176123123,
+      "email_address": "[email protected]",
+      "customer_probability": 0.1,
+      "life_time_value": 400000
+    },
+    {
+      "lead_id": 1,
+      "company_name": "test_company",
+      "first_name": "test_first",
+      "last_name": "test_last",
+      "country_code": "DE",
+      "phone_number": 176123123,
+      "email_address": "[email protected]",
+      "customer_probability": 0.9,
+      "life_time_value": 1000
+    },
+    {
+      "lead_id": 2,
+      "company_name": "test_company",
+      "first_name": "test_first",
+      "last_name": "test_last",
+      "country_code": "DE",
+      "phone_number": 176123123,
+      "email_address": "[email protected]",
+      "customer_probability": 0.7,
+      "life_time_value": 3500
+    },
+    {
+      "lead_id": 3,
+      "company_name": "test_company",
+      "first_name": "test_first",
+      "last_name": "test_last",
+      "country_code": "DE",
+      "phone_number": 176123123,
+      "email_address": "[email protected]",
+      "customer_probability": 0.4,
+      "life_time_value": 10000
+    },
+    {
+      "lead_id": 4,
+      "company_name": "test_company",
+      "first_name": "test_first",
+      "last_name": "test_last",
+      "country_code": "DE",
+      "phone_number": 176123123,
+      "email_address": "[email protected]",
+      "customer_probability": 0.32,
+      "life_time_value": 20000
+    }
+  ]
+}

src/database/dummy_leads.json.license

@@ -0,0 +1,2 @@
+SPDX-License-Identifier: CC-BY-4.0
+SPDX-FileCopyrightText: 2023 Felix Zailskas <[email protected]>

src/evp/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: MIT
+# SPDX-FileCopyrightText: 2023 Felix Zailskas <[email protected]>

src/evp/evp.py

@@ -0,0 +1,53 @@
+# SPDX-License-Identifier: MIT
+# SPDX-FileCopyrightText: 2023 Felix Zailskas <[email protected]>
+
+import numpy as np
+from sklearn.linear_model import LinearRegression
+
+from database import get_database
+
+
+class LeadValue:
+    def __init__(
+        self, lifetime_value: float = 0, customer_probability: float = 0
+    ) -> None:
+        assert (
+            0.0 <= customer_probability <= 1.0
+        ), "Probability of becoming a customer must be between 0.0 and 1.0"
+        self.life_time_value = lifetime_value
+        self.customer_probability = customer_probability
+
+    def get_lead_value(self) -> float:
+        return self.life_time_value * self.customer_probability
+
+
+class EstimatedValuePredictor:
+    def __init__(self) -> None:
+        self.probability_predictor = LinearRegression()
+        self.life_time_value_predictor = LinearRegression()
+
+        data = get_database().get_all_entries()
+        X = np.random.random((len(data), len(data)))
+        y_probability = np.array(
+            [item["customer_probability"] for item in data.values()]
+        )
+        y_value = np.array([item["customer_probability"] for item in data.values()])
+
+        self.probability_predictor.fit(X, y_probability)
+        self.life_time_value_predictor.fit(X, y_value)
+
+    def estimate_value(self, lead_id) -> LeadValue:
+        # make call to data base to retrieve relevant fields for this lead
+        lead_data = get_database().get_entry_by_id(lead_id)
+
+        # preprocess lead_data to get feature vector for our ML model
+        feature_vector = np.random.random((1, 5))
+
+        # use the models to predict required values
+        lead_value_pred = self.life_time_value_predictor.predict(feature_vector)
+        # manually applying sigmoid to ensure value in range 0, 1
+        cust_prob_pred = 1 / (
+            1 + np.exp(-self.probability_predictor.predict(feature_vector))
+        )
+
+        return LeadValue(lead_value_pred, cust_prob_pred)

src/evp_demo.py

@@ -0,0 +1,26 @@
+# SPDX-License-Identifier: MIT
+# SPDX-FileCopyrightText: 2023 Felix Zailskas <[email protected]>
+
+from database import get_database
+from evp.evp import EstimatedValuePredictor
+
+lead_id = 0
+
+lead_data = get_database().get_entry_by_id(lead_id)
+
+evp = EstimatedValuePredictor()
+lead_value = evp.estimate_value(lead_id)
+
+print(
+    f"""
+    Dummy prediction for {lead_id=}:
+
+    Data:
+    {lead_data}
+
+    This lead has a predicted probability of {lead_value.customer_probability} to become a customer.
+    This lead has a predicted life time value of {lead_value.life_time_value}.
+
+    This results in a total lead value of {lead_value.get_lead_value()}.
+"""
+)