Issue abhisheks008#440 SSOC

Automobile Sales Data Analysis and Prediction/Dataset/README.md

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+The dataset contains Sales data of an Automobile company.
+
+https://www.kaggle.com/datasets/ddosad/auto-sales-data/data

Automobile Sales Data Analysis and Prediction/Model/README.md

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+## 1. Simple Dense Neural Network (DNN):
+
+Description: A basic feedforward neural network where neurons are organized in layers, and each neuron in one layer connects to every neuron in the next layer. It does not inherently consider sequential or temporal patterns.
+Usage: Suitable for tasks where inputs are independent of each other or where feature engineering has already captured temporal dependencies. Less effective for time series data with inherent sequential relationships.
+
+## 2. Bidirectional LSTM (BiLSTM):
+
+Description: A variant of LSTM where two separate hidden states are used for the recurrent layer, allowing it to learn patterns from both directions of a sequence (forward and backward in time).
+Usage: Effective for tasks where understanding the context from both past and future data points is important, such as natural language processing (NLP), speech recognition, and time series prediction.
+
+## 3. Convolutional Neural Network (CNN):
+
+Description: Initially designed for image recognition, CNNs use convolutional layers to extract spatial hierarchies of features. In time series analysis, 1D convolutions can be used to capture patterns across sequential data.
+Usage: Suitable for extracting local patterns in sequential data, such as sensor data or audio signals. Can efficiently learn spatial hierarchies but may require careful tuning of kernel sizes and strides for optimal performance.
+
+## 4. Random Forest Regressor:
+
+Description: An ensemble learning method that constructs multiple decision trees during training and outputs the mean prediction of the individual trees for regression tasks.
+Usage: Effective for time series forecasting when features have complex interactions or nonlinear relationships. It handles missing values well and is less prone to overfitting compared to a single decision tree.
+
+## 5. Temporal Convolutional Network (TCN):
+
+Description: A modern deep learning architecture for sequence modeling using dilated causal convolutions. TCNs capture long-range dependencies and have shown effectiveness in various sequential tasks.
+Usage: Suitable for multivariate time series forecasting where both short-term and long-term dependencies need to be captured efficiently. TCNs can handle variable length inputs and are computationally efficient compared to traditional RNNs like LSTM.

Automobile Sales Data Analysis and Prediction/requirements.txt

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+numpy==1.26.4
+pandas==2.2.2
+matplotlib==3.9.0
+scikit-learn~=1.5.0