Predicting Unique Website Visitors With a Tensorflow Time Series Model

This project will use Tensorflow to create a time series model capable of making predictions on unique website visitor data. Data source:

https://www.kaggle.com/bobnau/daily-website-visitors

import pandas as pd import numpy as np import seaborn as sns import tensorflow as tf from tensorflow import keras import matplotlib.pyplot as plt from datetime import datetime

df = pd.read_csv('daily-website-visitors.csv') df = df[['Date', 'Unique.Visits']] df = df.rename(columns={'Unique.Visits': 'unique_visits', 'Date': 'date'}) df['date'] = pd.to_datetime(df.date)

df.info()

df.head()

Let's get the ball rolling with making sure the data is clean and usable. We know we're working with dates, so it's always a good idea to make sure these are in datetime format. We also see commas in the unique_visits numbers, which need to be removed before changing the data type to int from object.

df = df.replace(',', '', regex=True) df['unique_visits'] = df['unique_visits'].astype(int)

Tensorflow and Keras both love arrays, so it is a good idea to turn the data into np.arrays now, as we will need to do so later on anyways in order to feed data into our network model.

# Converting full df to array all_visits = np.array(df['unique_visits']) all_dates = np.array(df['date'])

Training and Validation Splits, Finding Baseline

Now we have our data cleaned and in the right format for analysis, let's get the Training and Validation splits set up. I chose to use data up to the 1st of Jan 2019 as training, and data post this date as validation. This is how I landed on index value 1571 as the cut-off position.

# Train and Validation Split split_time = 1571 visits_train = all_visits[:split_time] visits_test = all_visits[split_time:] dates_train = all_dates[:split_time] dates_test = all_dates[split_time:]

Let's have a look at the Naïve Forecast, where we take the previous period and use it to forecast the period ahead. We'll use the resulting Mean Absolute Error as our baseline accuracy to beat.

naive_forecast = all_visits[split_time - 1:-1]

def plot_series(time, series, format="-", start=0, end=None, color='blue'): plt.plot(time[start:end], series[start:end], format, color=color) plt.xlabel("Time") plt.ylabel("Value") plt.grid(True)

# Display plot of naive forecast plt.figure(figsize=(10, 6)) plot_series(dates_test, visits_test, start=0, end=150) plot_series(dates_test, naive_forecast, start=1, end=151, color='red') plt.show()

# Finding our baseline accuracy with MAE - results in an MAE of 515 print("Baseline accuracy is: ", keras.metrics.mean_absolute_error(visits_test, naive_forecast).numpy())

# Data Preparation - creating windowed tf.dataset.Datasets dataset = tf.data.Dataset.from_tensor_slices(visits_train) dataset = dataset.window(21, shift=1, drop_remainder=True) dataset = dataset.flat_map(lambda window: window.batch(21)) dataset = dataset.map(lambda window: (window[:-1], window[-1:])) dataset = dataset.shuffle(buffer_size=10) dataset = dataset.batch(1).prefetch(1)

Tensorflow Model Design

tf.keras.backend.clear_session() model = tf.keras.models.Sequential() layer0 = model.add(tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1), input_shape=[None])) layer1 = model.add(tf.keras.layers.Conv1D(filters=16, kernel_size=5, strides=1, padding='causal', activation='relu')) layer2 = model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(30, return_sequences=True))) layer2 = model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(30))) layer4 = model.add(tf.keras.layers.Dense(1)) layer5 = model.add(tf.keras.layers.Lambda(lambda x: x * 1000))

Setting a checkpoint callback to save the best model weights (lowest MAE):

# Model Callbacks checkpoint_filepath = '/work/model_checkpoint' model_checkpoint = tf.keras.callbacks.ModelCheckpoint( filepath=checkpoint_filepath, save_weights_only=True, monitor='mae', mode='min', save_best_only=True )

## SGD Optimizer - provided fairly bad results with losses around ~2000 # model.compile(loss="mae", optimizer=tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9)) ## Adam Optimizer - significantly better results, losses in the high 100's # model.compile(metrics=["mae"], optimizer=optimizer, loss=tf.keras.losses.Huber()) ## RMSprop - best optimizer yet - low 700s # model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=0.001), loss='mae') # optimizer = tf.keras.optimizers.Adam( # learning_rate=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False, # name='Adam') reduce_lr_loss = tf.keras.callbacks.ReduceLROnPlateau(monitor='mae', factor=0.0001, patience=6, verbose=1, min_delta=0.001, mode='min') earlyStopping = tf.keras.callbacks.EarlyStopping(monitor='mae', patience=10, verbose=0, mode='min') model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=0.0001), metrics=['mae'], loss=tf.keras.losses.MeanAbsolutePercentageError()) model.fit(dataset, epochs=100, verbose=1, callbacks=[model_checkpoint, reduce_lr_loss, earlyStopping])

# Loading the weights of a model trained on my local machine. model.load_weights(checkpoint_filepath)

Predictions

def get_preds(model, val_data): series = tf.expand_dims(val_data, axis=-1) tf_val_data = tf.data.Dataset.from_tensor_slices(series) tf_val_data = tf_val_data.window(21, shift=1, drop_remainder=False) tf_val_data = tf_val_data.flat_map(lambda x: x.batch(21)) tf_val_data = tf_val_data.batch(1).prefetch(1) forecast = model.predict(tf_val_data) # forecast = forecast * 10 forecast = np.abs(forecast) return forecast

preds = get_preds(model, visits_test) print(preds[:10]) print(visits_test[:10])

tf.keras.metrics.mean_absolute_error(visits_test, preds)

plt.figure(figsize=(20, 10)) plot_series(all_dates, all_visits) plot_series(dates_test, preds, color='red') plt.show()

plt.figure(figsize=(20, 10)) plot_series(dates_test, visits_test) plot_series(dates_test, preds, color='red') plt.show()

Predicting Unique Website Visitors With a Tensorflow Time Series Model

Training and Validation Splits, Finding Baseline

Tensorflow Model Design

Predictions

Conclusion

Hire Me

.css-15w88e5{color:var(--chakra-colors-fg-neutral-primary);font-weight:inherit;letter-spacing:-0.09px;}Predicting Unique Website Visitors With a Tensorflow Time Series Model

Training and Validation Splits, Finding Baseline

Tensorflow Model Design

Predictions

Conclusion

Hire Me

Predicting Unique Website Visitors With a Tensorflow Time Series Model