KevKibe/Inflation_in_Kenya_TimeSeries_Prediction

This is a time-series forecasting application used for predicting future inflation rate in Kenya using Deep Learning. # Inflation_in_Kenya_TimeSeries_Prediction ## Description - This is a time-series forecasting application used for predicting future inflation rate in Kenya. - This notebook shows how I trained the model ## Dataset The dataset is from this linkhttps://centralbank.go.ke. The columns in the dataset include: - 12-month inflation: normally considered as inflation rate, is defined as the percentage change in the monthly consumer price index (CPI). For example, the 12-month inflation rate for November 2017 is the percentage change in the CPI of November 2017 and November 2016. - Annual average inflation: is the percentage change in the annual average consumer price index (CPI) of the corresponding months e.g. November 2017 and November 2016. - Source: Kenya National Bureau of Statistics. ## Model -This is the model consists of a 1D convolutional layer, followed by three LSTM (Long Short-Term Memory) layers, and three dense layers, with a single unit output layer for prediction. model = tf.keras.models.Sequential([ tf.keras.layers.Conv1D(filters=64, kernel_size=3, strides=1, activation='relu', input_shape=[window_size,1]), tf.keras.layers.LSTM(128, return_sequences=True), tf.keras.layers.LSTM(128, return_sequences=True), tf.keras.layers.LSTM(128), tf.keras.layers.Dense(256, activation='relu'), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(64, activation="relu"), tf.keras.layers.Dense(1), ]) ## Results - The model achieved a Mean Squared Error of `1.28` and Mean Absolute Error of `0.89`. **A plot of the predicted vs the actual values on the Validation Set** - orange is predicted value, blue is actual value. **:zap: I'm currently open for roles in Data Science, Machine Learning, NLP and Computer Vision.**