there are several artificial intelligence tools that can be utilized to assess the demand for employees and forecast turnover but one of the most popular and robust options is tensor flow tensor flow is an open-source artificial intelligence Library developed and maintained by Google offering a range of features and extensibility let us consider this example we possess data on both current and former employees encompassing their work experience competencies salaries job satisfaction and more our objective is to develop a model capable of predicting which current employees May depart from the workplace in the near future that is exit forecasting the data is loaded from a CSV file which should include a column left indicating whether the employee left the company where one means that an employee left zero did not the neural network model encompasses several layers including the input layer multiple hidden layers employing the realu activation function and the output layer featuring the sigmoid activation function this Choice aligns with our aim of obtaining the probability of exit between 0 and 1 subsequently the model is compiled specifying the optimization algorithm loss function and accuracy metrics following this the model is trained using training data and evaluated with testing data the model. compile function serves to Define how the neural network training process will proceed setting three crucial parameters Optimizer denotes the algorithm utilized to update Network weights during learning atom is a popular Optimizer in deep neural networks valued for its speed and efficiency in train loss refers to the function used to calculate the difference between the Network's forecast and the true values during learning binary cross entropy is a commonly employed loss function for binary classification tasks ultimately a list of metrics employed to measure the efficacy of the learning process accuracy is frequently utilized for classification tasks to gauge how accurately the network classifies data hence the model mod. pile function determines the configuration of the training process encompassing Optimizer loss and metrics this step is crucial to defining how the neural network will be trained and evaluated during the learning process regarding the specifics 50 epics signify that the neural network will undergo training 50 times across the entire data set an epic represents one complete iteration over the data set used for training batch size 32 indicates that the training data will be partitioned into small batches of 32 samples each smaller batch sizes are commonly employed to enhance training efficiency split equal to 0.2 denotes that the training data will be divided into two parts with 80% utilized for training and 20% for validation the validation set is solely employed to EV valuate the model's performance during training splitting a data set into training and validation sets is a fundamental practice in machine learning model development the purpose of splitting a data set is to evaluate the performance of a machine learning model the training set is used to train the model while the validation set is used to assess how well the model generalizes to unseen data if we were to use the same data for both training and validation the models performance might be overly optimistic as it could simply memorize the training data instead of learning patterns that generalize to new data next by evaluating the model on a separate validation set we can detect overfitting overfitting occurs when a model learns to fit the training data too closely capturing noise and random fluctuations rather than underlying patterns if the model performs well on the training set but poorly on the validation set it's a sign that it has overfit the training data furthermore we use the validation set to compare different hyperparameter settings and select the ones that result in the best performance machine learning models often have hyperparameters that need to be tuned to optimize performance hyperparameters are settings that are not learned from the data but rather set before the training process begins for example learning rate or regularization strength in machine learning learning rate determines how quickly or slowly the model updates its internal parameters based on the training data a high learning rate means the model learns quickly but might be less stable while a low learning rate means the model learns more slowly but is more stable when we talk about regularization strength we're essentially talking about how much we want to penalize the model for being too complex a higher regularization strength means we're being stricter about keeping the model simpler which can help prevent it from overfitting the training data ultimately by splitting the data before any manipulations we ensure that the validation set remains completely separate from the training set minimizing the risk of data leakage data leakage refers to any scenario where information from the validation set inadvertently influences the training process this can lead to overly optimistic performance estimates so splitting a data set into training and validation sets is necessary for unbiased model evaluation preventing overfitting tuning hyperparameters and avoiding data leakage it should be done prior to any manipulations with the data to maintain the Integrity of the evaluation process coming back to the summary of our model it will undergo training over 50 epochs with the training data divided into batches of 32 samples each while the validation set will monitor the model's performance throughout training these parameters contribute to ensuring an effective training process and the selection of an appropriate model thank you for watching this video