From 159051a785c203a90114b30abc6fd6cc4f00f5a8 Mon Sep 17 00:00:00 2001 From: Anirban Mukherjee Date: Mon, 11 Oct 2021 23:15:56 +0530 Subject: [PATCH] Update README.md - Changed path of data - Changed import of common\utils.py --- 7-TimeSeries/3-SVR/README.md | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/7-TimeSeries/3-SVR/README.md b/7-TimeSeries/3-SVR/README.md index 0b7d8336..33871793 100644 --- a/7-TimeSeries/3-SVR/README.md +++ b/7-TimeSeries/3-SVR/README.md @@ -2,7 +2,7 @@ In the previous lesson, you learned how to use ARIMA model to make time series predictions. Now you'll be looking at Support Vector Regressor model which is a regressor model used to predict continuous data. -## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/51/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/51/) ## Introduction @@ -28,6 +28,11 @@ Open the _/working_ folder in this lesson and find the _notebook.ipynb_ file.[^2 1. Run the notebook and import the necessary libraries: [^2] + ```python + import sys + sys.path.append('../../') + ``` + ```python import os import warnings @@ -42,13 +47,13 @@ Open the _/working_ folder in this lesson and find the _notebook.ipynb_ file.[^2 from common.utils import load_data, mape ``` -4. Load the data from the `/data/energy.csv` file into a Pandas dataframe and take a look: [^2] +2. Load the data from the `/data/energy.csv` file into a Pandas dataframe and take a look: [^2] ```python - energy = load_data('./data')[['load']] + energy = load_data('../../data')[['load']] ``` - -5. Plot all the available energy data from January 2012 to December 2014: [^2] + +3. Plot all the available energy data from January 2012 to December 2014: [^2] ```python energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12) @@ -58,7 +63,7 @@ Open the _/working_ folder in this lesson and find the _notebook.ipynb_ file.[^2 ``` ![full data](images/full-data.png) - + Now, let's build our SVR model. ### Create training and testing datasets