Merge pull request #1 from aiwithqasim/work

Added beginner friendly example of diabetes dataset

examples/README.md

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+### TensorFlow Decision Forests (TF-DF)
+
+
+| Tutorial                                    | Description                                                                                                 |
+|---------------------------------------------|-------------------------------------------------------------------------------------------------------------|
+| [minimal](./minimal.py)                     | display and evaluate a Random Forest model on the adult dataset                                             |
+| [beginner_diabetes](./beginner_diabetes.py) | Beginner-friendly  usage example of TensorFlow Decision Forests (TF-DF) using pima India's Diabetes dataset |
+

examples/beginner_diabetes.py

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+# Copyright 2022 Google LLC.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Beginner-friendly  usage example of TensorFlow Decision Forests (TF-DF).
+
+This example trains, display and evaluate a Random Forest model on the pima India's Diabetes dataset
+
+This example works with the pip package.
+
+Usage example (in a shell):
+
+  pip3 install tensorflow_decision_forests
+  python3 beginner_diabetes.py
+
+More examples are available in the documentation's colabs.
+"""
+
+"""About
+
+TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training,
+ serving and interpretation of Decision Forest models. The library is a collection of Keras models 
+ and supports classification, regression and ranking.
+ for more details [link](https://pypi.org/project/tensorflow-decision-forests/)
+"""
+
+# Installing the tensorflow_decision_forests
+# NOTE: Uncomment the below command If you don't have tensorflow_decision_forests package
+# !pip install tensorflow_decision_forests
+
+# Python libraries
+# Classic,data manipulation and linear algebra
+import pandas as pd
+import numpy as np
+
+# Data processing, metrics and modeling
+import tensorflow_decision_forests as tfdf
+
+# Check the current version of TensorFlow Decision Forests
+print("Found TF-DF v" + tfdf.__version__)
+
+"""# NOTE:
+This notebook is to train the same model that I had trained in July 2021 using Decision Tree algorithm 
+[Pima Indians Diabetes - EDA & Prediction](https://www.kaggle.com/code/qasimhassan/eda-decision-tree) 
+but now in this notebook  will all about how to use **TensorFlow Decision Forests (TF-DF**). 
+You can access the dataset from this [link](https://www.kaggle.com/code/qasimhassan/eda-decision-tree/data)
+"""
+
+# loading dataset
+pima = pd.read_csv(".datasets/diabetes.csv")
+
+pima.head()
+
+#selecting the important features and target variable
+feature_cols = ['Insulin', 'BMI', 'Age','Glucose','BloodPressure','DiabetesPedigreeFunction', 'Outcome']
+dataset_df = pima[feature_cols]
+
+# Split the dataset into a training and a testing dataset into 70-30 ratio.
+test_indices = np.random.rand(len(dataset_df)) < 0.30
+test_ds_pd = dataset_df[test_indices]
+train_ds_pd = dataset_df[~test_indices]
+print(f"{len(train_ds_pd)} examples in training"
+      f", {len(test_ds_pd)} examples for testing.")
+
+# Converts a Pandas dataset into a tensorflow dataset
+train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_ds_pd, label="Outcome")
+test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_ds_pd, label="Outcome")
+
+# Trains the model.
+model = tfdf.keras.RandomForestModel(verbose=2)
+model.fit(x=train_ds)
+
+# Summary of the model structure.
+model.summary()
+
+# Evaluate the model on the validation dataset.
+model.compile(metrics=["accuracy"])
+evaluation = model.evaluate(test_ds)
+
+# Export the model to the SavedModel format for later re-use e.g. TensorFlow
+# Serving.
+model.save("/temp/my_saved_model")
+
+# Look at the feature importances.
+model.make_inspector().variable_importances()
+
+"""Comparison
+
+When I had used Simple Decision Tree from sklearn after optimizing the final testing accuracy that I got 
+was 77% (reference: [check link](https://www.kaggle.com/code/qasimhassan/eda-decision-tree)) 
+but by using TensorFlow Decision Forests (TF-DF) I got the testing accuracy of about 81%.
+"""
+