diff --git a/docs/docs/telco.md b/docs/docs/telco.md
new file mode 100644
index 00000000..f42381de
--- /dev/null
+++ b/docs/docs/telco.md
@@ -0,0 +1,85 @@
+---
+sidebar_position: 4
+---
+
+# Let's build a model predicting company churn
+
+Here we are going to walk through an example where we use scikit.js to predict churn at a major telecomunications company. We will be using the dataset here (Kaggle link).
+
+It's hosted remotely here (though we should probably host it as well).
+
+## Examining the data
+
+First let's create a new directory called `telco` that we are going to perform JS magic in. Let's also initialize npm, so that we can download our packages
+
+> > > mkdir telco
+> > > cd telco
+> > > npm init
+
+For this analysis, we are going to use `danfojs-node`, and `scikitjs`. So let's install them.
+
+> > > npm install danfojs-node scikitjs
+
+```javascript
+import * as dfd from 'danfojs-node'
+import {
+  LogisticRegression,
+  OneHotEncoder,
+  makeColumnTransformer,
+  StandardScaler,
+  makePipeline
+} from 'scikitjs/node'
+
+/**
+ * First let's read in the data and take a look at it.
+ */
+let df = await dfd.readCSV(
+  'https://raw.githubusercontent.com/rajeevratan84/datascienceforbusiness/master/WA_Fn-UseC_-Telco-Customer-Churn.csv'
+)
+
+/**
+ * The first thing I like to do is figure out the types of the input
+ * We can scour the df.ctypes.print() to see what the types are, and also use
+ * df.
+ * It looks like there are some useless categories.
+ * customerID has only unique values (none are repeated) and so therefor it
+ * can be safely ignored.
+ *
+ * Let's check for any missing values. It looks like there are none.
+ *
+ * By using the unique function we can easily see which columns are categorical
+ * or not. Or my inspecting the danfo column types. df.ctypes.print()
+ */
+
+let categorical = [
+  'gender',
+  'Partner',
+  'Dependents',
+  'PhoneService',
+  'Contract',
+  'PaperlessBilling',
+  'PaymentMethod',
+  'MultipleLines',
+  'InternetService',
+  'OnlineSecurity',
+  'TechSupport',
+  'StreamingTV',
+  'StreamingMovies',
+  'OnlineBackup',
+  'DeviceProtection'
+]
+
+let numerical = ['SeniorCitizen', 'tenure', 'MonthlyCharges', 'TotalCharges']
+
+let y = df['Churn']
+
+let colTransform = makeColumnTransformer([
+  [new OneHotEncoder(), categorical],
+  [new StandardScaler(), numerical]
+])
+
+let lr = makePipeline(colTransform, new LogisticRegression())
+await lr.fit(df, y)
+console.log(lr.coef)
+console.log(lr.score())
+```