Answer / Tikam Singh

To achieve accuracy in your first model, you should focus on the following steps:

1. Data Preprocessing: Clean and preprocess your data to remove any noise, missing values or outliers. This will ensure that your model is trained on clean and reliable data.

2. Feature Engineering: Create relevant features from the raw data that can help in improving the accuracy of the model. This could involve extracting trends, creating new variables, or binning continuous variables.

3. Model Selection: Choose a suitable machine learning algorithm based on the nature of your data and problem at hand. Try multiple models and compare their performance to find the best one.

4. Hyperparameter Tuning: Adjust the hyperparameters of your chosen model to optimize its performance. This could involve changing the learning rate, regularization strength, number of hidden layers, etc.

5. Validation: Split your data into training, validation, and testing sets to evaluate the performance of your model on unseen data. Use the validation set to fine-tune your model and avoid overfitting.

6. Cross-Validation: Implement cross-validation techniques to ensure that your model performs well not only on the training set but also on different subsets of the data.

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