diff --git a/ml-tests/README.md b/ml-tests/README.md
new file mode 100644
index 0000000..332d9dc
--- /dev/null
+++ b/ml-tests/README.md
@@ -0,0 +1,30 @@
+# Insurance Advisor
+
+Lakehead University COMP-9800 Project Winter - Spring/Summer 2021
+
+## Steps to install
+
+1. Install Anaconda
+
+2. Install the xgboost
+
+```
+$ pip install xgboost
+```
+
+## Steps to run
+1. To run the notebook on Anaconda, Click on “Cells” and click on “Run all”
+
+2. To run the notebook on Google Colab, Click on "Runtime" and click on “Run all”
+
+3. To run all the models from .py file
+
+```
+$ python insurance.py
+```
+
+4. To only execute Voting Regressor with 2 GBRs from .py file
+
+```
+$ python gbr.py
+```
diff --git a/ml-tests/gbr.py b/ml-tests/gbr.py
new file mode 100644
index 0000000..c0b9087
--- /dev/null
+++ b/ml-tests/gbr.py
@@ -0,0 +1,64 @@
+from pandas import *
+from math import ceil
+from sklearn.ensemble import GradientBoostingRegressor, VotingRegressor
+from sklearn.metrics import mean_absolute_error
+from sklearn.model_selection import train_test_split
+import numpy as np
+
+# Load the data
+print('Reading the data...')
+data = read_csv("insurance.csv")
+print('Read completed.\n')
+
+# One-hot encoding
+print('Preprocessing data...')
+data = get_dummies(data, columns=['sex', 'smoker', 'region'], drop_first=True)
+
+# Format and Split the data
+x = data[['age', 'bmi', 'children', 'sex_male', 'smoker_yes', 'region_northwest', 'region_southeast', 'region_southwest']]
+y = data['charges']
+
+train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.4)
+print('Preprocessing completed.\n')
+
+# Train the model and make predictions
+r1 = GradientBoostingRegressor(loss='huber', learning_rate=0.13, max_features='auto', alpha=0.7, random_state=1)
+r2 = GradientBoostingRegressor(loss='huber', learning_rate=0.13, max_features='auto', alpha=0.7, random_state=1)
+model = VotingRegressor([('gbr1', r1), ('gbr2', r2)])
+model.fit(train_x, train_y)
+
+print('Testing the model...')
+predicted = model.predict(test_x)
+mae = mean_absolute_error(test_y, predicted)
+print('Mean Absolute Error : ',mae)
+print('Testing completed.\n')
+
+# Predict cost for a sample customer
+print('Running for one sample...')
+sample = DataFrame({
+ 'age': 26,
+ 'bmi': 25.44,
+ 'children': 1,
+ 'sex_male': 1,
+ 'smoker_yes' : 0,
+ 'region_northeast': 0,
+ 'region_southeast': 0,
+ 'region_southwest': 1,
+ }, [1])
+print('Sample data : ',sample)
+cost = model.predict(sample)[0]
+print('Predicted cost : ', cost)
+print('Sample run completed.\n')
+
+print('Calculating premium...')
+# Calculate premium
+def compute_monthly_premium(cost):
+ multiplier = 1.1
+ return ceil(cost*multiplier)/12
+
+print('Monthly Premium : ',compute_monthly_premium(cost))
+print('Premium calculated.\n')
+
+print('Program completed.')
+
+print('Mean Absolute Error : ',mae)
diff --git a/ml-tests/insurance.csv b/ml-tests/insurance.csv
new file mode 100644
index 0000000..24f6205
--- /dev/null
+++ b/ml-tests/insurance.csv
@@ -0,0 +1,1339 @@
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diff --git a/ml-tests/insurance.ipynb b/ml-tests/insurance.ipynb
new file mode 100644
index 0000000..b797574
--- /dev/null
+++ b/ml-tests/insurance.ipynb
@@ -0,0 +1,891 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5DerHkqDVHdn"
+ },
+ "outputs": [],
+ "source": [
+ "from keras.models import Sequential\n",
+ "from keras.layers import Dense\n",
+ "import matplotlib.pyplot as plt\n",
+ "from pandas import *\n",
+ "from sklearn.ensemble import AdaBoostRegressor, BaggingRegressor, ExtraTreesRegressor, GradientBoostingRegressor, RandomForestRegressor, VotingRegressor\n",
+ "from sklearn.linear_model import BayesianRidge, ElasticNet, Lasso, LassoLars, LinearRegression, Ridge, SGDRegressor\n",
+ "from sklearn.metrics import mean_absolute_error\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.neighbors import KNeighborsRegressor\n",
+ "from sklearn.pipeline import make_pipeline\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.svm import SVR\n",
+ "from sklearn.tree import DecisionTreeRegressor\n",
+ "from xgboost import XGBRegressor\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ },
+ "id": "1CboErVQeTcj",
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