README Enhancement - Avocados Classification

Avocados Classification/README.md

@@ -1,25 +1,98 @@
-# Avocados Classification
-## SOCIAL SUMMER OF CODE-2023 /World-of-ML/DL-Simplified#361
-
-
-Full name : Harsh Raj
-
-GitHub Profile Link : https://github.com/RAJharsh02
-
-Email ID : harshraj2828@gmail.com
-
-
-What is your participant role? SSOC23
-
-
-
-Approach for this Project:The project aimed to predict whether an avocado is conventional or organic. using Deep learning models. The dataset was loaded and explored to gain insights into the data, followed by preprocessing steps such as handling missing values and feature scaling. Three models were developed: Feedforward Neural Network (FNN),TabNet, and Long Short-Term Memory (LSTM). Each model was trained and evaluated based on accuracy.
-
-## Models:-
-![carbon (10)](https://github.com/RAJharsh02/Avocados-classification/assets/118257196/e23ec958-273d-4b90-b886-a524c1a19361)
-![carbon (11)](https://github.com/RAJharsh02/Avocados-classification/assets/118257196/aa719f26-aaa6-404a-9821-6c33f1f90e5b)
-![carbon (12)](https://github.com/RAJharsh02/Avocados-classification/assets/118257196/05bf34fc-0749-470f-ab85-48298aee2d51)
-
-
-## Accuracy Score of different Model:
-![Comparison graph](https://github.com/RAJharsh02/Avocados-classification/assets/118257196/60225a7c-e5eb-4904-8ec4-db7f079e2d60)
+<h1>Avocados Classification</h1>
+
+🎯**GOAL**
+
+The project aims to predict whether an avocado is conventional or organic using Deep learning models.
+
+🧵**DATASET**
+
+https://www.kaggle.com/datasets/neuromusic/avocado-prices
+
+🧾**DESCRIPTION**
+
+The main goal of this project is to develop a Dl model that can accurately predict whether an avocado is conventional or organic based on various features such as `Size of Bags`,`Volume`,`Price` etc. The purpose of this project is to categorize avocados for better market segmentation and analysis
+
+🧮**WHAT I HAD DONE**
+
+
+1. Data loading and exploration: Loaded the dataset, examined its structure, and performed initial exploratory data analysis (EDA) to gain insights into the data distribution, missing values, and relationships between variables.
+
+2. Data preprocessing: Conducted data preprocessing steps such as handling missing values, encoding categorical variables, and scaling numerical features to prepare the data for model training.
+
+3. Feature selection: Applied feature selection techniques to identify the most relevant features that contribute significantly to the prediction of heart strokes. This helps in reducing model complexity and improving performance.
+
+4. Model development: Three models were developed: Feedforward Neural Network (FNN),TabNet, and Long Short-Term Memory (LSTM). Each model was trained and evaluated based on accuracy.
+
+ 🚀**Models Implemented**
+```bash
+  a.LSTM (Long Short-Term Memory In avocado classification, LSTM 
+  classifies by taking sequential input data and processing it through 
+  memory cells, which learn to retain relevant information. It builds the 
+  model using multiple LSTM layers to capture complex patterns, and then 
+  applies a dense layer for final classification based on the learned 
+  temporal dependencies, resulting in accurate avocado classification.
+```
+```bash
+  b. Feedforward Neural Network (FNN): Developed an FNN model using the Keras library 
+  with multiple hidden layers and appropriate activation functions. Trained the model using  
+  the preprocessed data and fine-tuned hyperparameters to achieve optimal performance.
+```
+```bash
+  c. Recurrent Neural Network (RNN): TABNET classifies using a unique 
+  attention mechanism that selects and updates relevant features during 
+  training. It builds the model by iteratively selecting subsets of 
+  features, employing shared decision trees, and gradually learning 
+  feature importance, leading to an interpretable and efficient model 
+  for accurate avocado classification..
+```
+📚**LIBRARIES NEEDED**
+
+- Pandas
+- Tensorflow
+- Seaborn
+- Sklearn
+- pathlib
+- numpy
+- keras
+- torch
+
+📈**Performance of the Models based on the Accuracy Scores**
+
+| Model            | Accuracy                                                               |
+| ----------------- | ------------------------------------------------------------------ |
+| TabNet |  0.79 |
+| LSTM |9389041095890411 |
+| FNN| 1.0 |
+
+**TabNet Comparison Evaluation**
+<br>
+![Tabnet comparison evaluation](https://github.com/abhisheks008/DL-Simplified/blob/1630e48a26b392ea03f882270222dab253e8470e/Avocados%20Classification/Images/TabNet%20Comparison%20evaluation.png)
+<br>
+<br>
+**LSTM Comparison Evaluation**
+<br>
+![Lstm comaprison evaluation](https://github.com/abhisheks008/DL-Simplified/blob/1630e48a26b392ea03f882270222dab253e8470e/Avocados%20Classification/Images/LSTM%20comparison%20evaluation.png)
+<br>
+<br>
+**FNN Comparison Evaluation**
+<br>
+![FNN Comaprison Evaluation](https://github.com/abhisheks008/DL-Simplified/blob/1630e48a26b392ea03f882270222dab253e8470e/Avocados%20Classification/Images/FNN%20Comparison%20evaluation.png)
+
+<br>
+
+![Comparison graph](https://github.com/RAJharsh02/Avocados-classification/assets/118257196/60225a7c-e5eb-4904-8ec4-db7f079e2d60)
+
+📢**CONCLUSION**
+
+In conclusion, this project aimed to classify Avocados using DL models. Among the models developed, the Feedforward Neural Network (fNN) achieved the highest accuracy of 100%. This suggests that the temporal dependencies captured by the FNN architecture are valuable in slassifying Avocados.
+
+✒️**AUTHOR**
+
+- Code contributed by *Harsh Raj* @ #SSoC_2023 <br>
+Email : harshraj2828@gmail.com <br>
+[![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/RAJharsh02)
+
+- README.md modified by *Mariam* @ #DWoC_2023
+
+[![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/mariam-m7084)  [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/mariam7084/)
+