Commit 3dc9bc84 authored by rtugade's avatar rtugade
Browse files

Updated css for some cells

parent b51046bb
......@@ -189,48 +189,59 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Methodology"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data Preprocessing\n",
"<div style=\"width: 90%\">\n",
"\n",
"<p style=\"text-align:left; font-size: 20px; font-family: Times New Roman, Times, serif\">\n",
"Methodology\n",
"</p>\n",
"\n",
"<p style=\"text-align:left; font-size: 18px; font-family: Times New Roman, Times, serif\">\n",
"Data Processing\n",
"</p>\n",
"\n",
"<p style=\"text-align:justify; font-family: Times New Roman, Times, serif\">\n",
"Features selected were year, holiday, temp, hum, windspeed, season, weathersit, mnth, hr, and weekday. The target variable was cnt. One-hot encoding was then applied on season, weathersit, mnth, hr, and weekday. The features data contained 56 features. The features were then scaled using min-max scaling given by:\n",
"</p>\n",
"\n",
"\\begin{equation}\n",
"X_{scaled} = \\frac{X - min(X)}{max(X) - min(X)}\n",
"\\end{equation}\n",
"\n",
"where X is the feature matrix. Since the maximum value of cnt was found to be 977, cnt was divided by 1000 to scale it to values between 0 and 1. \n",
"<p style=\"text-align:justify; font-family: Times New Roman, Times, serif\">\n",
"where X is the feature matrix. Since the maximum value of cnt was found to be 977, cnt was divided by 1000 to scale it to values between 0 and 1. The last 20 days were set for testing set. In the remaining dataset, the last 60 days were set as the validation set. Finally, the remaining dataset was used as training set. The training set was used to train the machine learning models. The validation set was used to evaluate the model during training, and finally, the testing set was used to test the accuracy of the model after training using the best parameters.\n",
"</p>\n",
"\n",
"The last 20 days were set for testing set. In the remaining dataset, the last 60 days were set as the validation set. Finally, the remaining dataset was used as training set. \n",
"\n",
"The training set was used to train the machine learning models. The validation set was used to evaluate the model during training, and finally, the testing set was used to test the accuracy of the model after training using the best parameters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Neural Network Modeling\n",
"<p style=\"text-align:left; font-size: 18px; font-family: Times New Roman, Times, serif\">\n",
"Neural Network Modeling\n",
"</p>\n",
"\n",
"\n",
"<p style=\"text-align:justify; font-family: Times New Roman, Times, serif\">\n",
"A feed-forward neural network having 56 input nodes, 56 nodes in one hidden layer, and 1 output node was developed. The learning rate from input to hidden and hidden to output were 0.001 and 0.0001 respectively. The loss function used is given by:\n",
"</p>\n",
"\n",
"\\begin{equation}\n",
"\\frac{1}{2}(\\Psi_{NN} - \\Psi_{true})^2\n",
"\\end{equation}\n",
"\n",
"where $\\Psi_{NN}$ is the predicted value and $\\Psi_{true}$ is the true value. The input, hidden, and output activation functions were linear, sine, and sigmoid respectively. The neural network was trained and validated using the training and validation sets over 12,000 iterations. The testing set was used to evaluate the predictive accuracy of the model. The accuracy metric used was the coefficient of determination, $r^2$."
"<p style=\"text-align:justify; font-family: Times New Roman, Times, serif\">\n",
"where $\\Psi_{NN}$ is the predicted value and $\\Psi_{true}$ is the true value. The input, hidden, and output activation functions were linear, sine, and sigmoid respectively. The neural network was trained and validated using the training and validation sets over 12,000 iterations. The testing set was used to evaluate the predictive accuracy of the model. The accuracy metric used was the coefficient of determination, $r^2$.\n",
"</p>\n",
"\n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Machine Learning Models\n",
"<div style=\"width: 90%\">\n",
"\n",
"<p style=\"text-align:left; font-size: 20px; font-family: Times New Roman, Times, serif\">\n",
"Machine Learning Models\n",
"</p>\n",
"\n",
"\n",
"Eight more regression models were trained on the same training dataset, namely k nearest neighbors (kNN), linear regression, lasso regression, ridge regression, linear support vector machines (LSVM), decision tree, random forest, and gradient boosting machines (GBM).\n",
"\n",
......@@ -246,7 +257,9 @@
"|LSVM|C|\n",
"|Decision tree|max depth|\n",
"|Random forest|max depth|\n",
"|GBM|max depth|"
"|GBM|max depth|\n",
"\n",
"</div>"
]
},
{
......
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