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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab.research.google.com/github/LukasPurbaW/100_Days_of_Deep_Learning/blob/main/Day_4_Simple_Prediction_Pipeline_(No_code).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

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# There are several process that have to be followed for applying deep learning / creating our deep learning model.
# The pipeline is almost the same as sklearn machine learning regression pipeline

# + [markdown] id="ILfu4fUOgu3o"
# ## 1. Data Preprocessing

# + id="wMabhUCLgwtt"
# In this step you should
# 1. Import the dataset.
# 2. Clean the dataset from missing value.
# 3. Removing useless columns.
# 4. Deciding which variable you want to predict.
# 5. Deciding which variable that cna be a predictor.
# 6. Encode the string data, you can use label encoder, or one hot encoding.
# 7. Splitting the dataset into train and test set.
# 8. Feature Scalling, simply doing min-max normalization or standarization it's up to you.

# + [markdown] id="Sdhbs2YThM8A"
# ## 2. Building the ANN

# + id="wi_TfgENh0dN"
# 1. Initializing the ANN.
# 2. Adding the input layers.
# 3. Adding the hidden layers.
# 4. Adding the output layers.

# + [markdown] id="<KEY>"
# ## 3. Training the ANN

# + id="04Wxr6EzieWs"
# 1. Compiling the ANN.
# 2. Training the ANN on Training Set

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# ## 4. Making the predictions and evaluating the model

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# 1. Predict it on test set.
# 2. Making the confusion matrix.
# 3. Predict a single result

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