Merge pull request #669 from why-aditi/main

ACI IoT Network Traffic Dataset Analysis

ACI IoT Network Traffic Dataset Analysis/Dataset/README.md

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+Link: https://www.kaggle.com/datasets/emilynack/aci-iot-network-traffic-dataset-2023

ACI IoT Network Traffic Dataset Analysis/Model/README.md

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+## **ACI IoT Network Traffic Dataset Analysis**
+
+### 🎯 **Goal**
+
+Analyze the traffic dataset
+
+### 🧵 **Dataset**
+
+https://www.kaggle.com/datasets/emilynack/aci-iot-network-traffic-dataset-2023
+
+### 🧾 **Description**
+
+The project aims to analyze the ACI IoT Network Traffic Dataset 2023 to identify patterns and anomalies in network traffic. The goal is to build an accurate predictive model for network anomaly detection.
+
+### 🧮 **What I had done!**
+
+Load the data using appropriate tools and conduct an initial inspection to identify missing values and outliers. Perform exploratory data analysis (EDA) to understand feature distributions and relationships. Clean the data by handling missing values and outliers, and engineer new features if necessary. Split the data into training and testing sets, scaling features as needed. Build and evaluate various models. Finalize the best model, evaluate it on the test set, and prepare it for deployment. Document each step and report the findings to ensure clarity and reproducibility.
+### 🚀 **Models Implemented**
+
+1. Random Forest Classifier
+2. XGBoost
+3. SVM
+4. KNN
+5. Decision Tree
+6. Dense Model
+
+### 📚 **Libraries Needed**
+
+1. numpy
+2. Pandas
+3. Matplotlib
+4. sci-kit learn
+
+### 📊 **Exploratory Data Analysis Results**
+
+<img src = "https://github.com/why-aditi/ML-Crate/blob/main/ACI%20IoT%20Network%20Traffic%20Dataset%20Analysis/Images/Bar%20Graph.png"/>
+<img src = "https://github.com/why-aditi/ML-Crate/blob/main/ACI%20IoT%20Network%20Traffic%20Dataset%20Analysis/Images/Pie%20Chart.png"/>
+
+### 📈 **Performance of the Models based on the Accuracy Scores**
+
+1. Random Forest Classifier: 99.77%
+2. XGBoost: 99.79%
+3. SVM: 99.99%
+4. KNN: 99.99%
+5. Decision Tree: 100%
+6. Dense Model: 99.80%
+
+
+### 📢 **Conclusion**
+
+Decision Tree is proven to be the best model with the accuracy score of 100%
+
+### ✒️ **Your Signature**
+
+Aditi Kala

ACI IoT Network Traffic Dataset Analysis/requirements.txt

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+sci-kit learn
+matplotlib
+numpy
+pandas