diff --git a/model.ipynb b/ProgrammingAssignment_1/model.ipynb
similarity index 89%
rename from model.ipynb
rename to ProgrammingAssignment_1/model.ipynb
index 735ca8c97857ec0465c8ad84566ff2c9bf775e6c..11f5f312809f9c848e95f3af250be014be25b6bf 100644
--- a/model.ipynb
+++ b/ProgrammingAssignment_1/model.ipynb
@@ -21,13 +21,6 @@
     "We'll use this skeleton for implementing different supervised learning algorithms. Please complete \"preprocess\" and \"partition\" methods below."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This step is for reading the dataset and for extracting features and labels. The \"preprocess\" function should return an *n x d* \"features\" array, and an *n x 1* \"labels\" array, where *n* is the number of examples and *d* is the number of features in the dataset. In cases where there is a big difference between the scales of features, we want to normalize the features to have values in the same range [0,1]. Since this is not the case with this dataset, we will not do normalization."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 14,
@@ -59,13 +52,6 @@
     "    return features, labels"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, you'll need to split your dataset into training, validation and test sets. The \"partition\" function should take as input the size of the whole dataset and randomly sample a proportion *t* of the dataset as test partition and a proportion of *v* as validation partition. The remaining will be used as training data. For example, to keep 30% of the examples as test and %10 as validation, set *t* = 0.3 and *v* = 0.1. You should choose these values according to the size of the data available to you. The \"split\" function should return indices of the training, validation and test sets. These will be used to index into the whole training set."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/model_solution.ipynb b/ProgrammingAssignment_1/model_solution.ipynb
similarity index 89%
rename from model_solution.ipynb
rename to ProgrammingAssignment_1/model_solution.ipynb
index eae4fa43111054cd8c29a583a5533c565b11018d..8e1f78b9b8ba4c77fe134126e7c004459a988f82 100644
--- a/model_solution.ipynb
+++ b/ProgrammingAssignment_1/model_solution.ipynb
@@ -21,13 +21,6 @@
     "We'll use this skeleton for implementing different supervised learning algorithms. Please complete \"preprocess\" and \"partition\" methods below."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This step is for reading the dataset and for extracting features and labels. The \"preprocess\" function should return an *n x d* \"features\" array, and an *n x 1* \"labels\" array, where *n* is the number of examples and *d* is the number of features in the dataset. In cases where there is a big difference between the scales of features, we want to normalize the features to have values in the same range [0,1]. Since this is not the case with this dataset, we will not do normalization."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 14,
@@ -58,13 +51,6 @@
     "    return features, labels"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, you'll need to split your dataset into training, validation and test sets. The \"partition\" function should take as input the size of the whole dataset and randomly sample a proportion *t* of the dataset as test partition and a proportion of *v* as validation partition. The remaining will be used as training data. For example, to keep 30% of the examples as test and %10 as validation, set *t* = 0.3 and *v* = 0.1. You should choose these values according to the size of the data available to you. The \"split\" function should return indices of the training, validation and test sets. These will be used to index into the whole training set."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,