Rename and add html and pdf

Machine-Learning.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "3116b75f",
    "metadata": {
     "jupyter": {
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "d159117377f3633c",
    "metadata": {},
    "outputs": [],
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "986fbb31a7ae0d8b",
    "metadata": {
     "jupyter": {
@@ -164,13 +164,12 @@
    "metadata": {},
    "source": [
     "### Adding missing Information\n",
-    "Some Games might not have any descriptions. For these we Input an Empty String\n",
-    "**TODO: check if dropna and fillna numeric_only is needed, as we dont have any numbers**"
+    "Some Games might not have any descriptions. For these we Input an Empty String."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "44239f6b7fd23cde",
    "metadata": {},
    "outputs": [],
@@ -197,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "ebc5a24e9bc87fdd",
    "metadata": {},
    "outputs": [
@@ -233,7 +232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "d2c3527a5fc876bf",
    "metadata": {},
    "outputs": [
@@ -280,12 +279,12 @@
    "metadata": {},
    "source": [
     "### Structurizing Text\n",
-    "If we want our Model to be able to use text as an input, we have to vectorize the text. TF-IDF (Inverse Document Frequency) is an easy way of transforming each word into a feature with a 0 to 1 value. **TODO: filter out stopwords**"
+    "If we want our Model to be able to use text as an input, we have to vectorize the text. TF-IDF (Inverse Document Frequency) is an easy way of transforming each word into a feature with a 0 to 1 value."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "4e8b407c",
    "metadata": {},
    "outputs": [
@@ -330,7 +329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "86d9da42f4df8e49",
    "metadata": {},
    "outputs": [],
@@ -353,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "e1bc73d4",
    "metadata": {},
    "outputs": [
@@ -385,7 +384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "4919bf1b37d171a7",
    "metadata": {},
    "outputs": [
@@ -417,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "cfbf3787",
    "metadata": {
     "jupyter": {
@@ -441,17 +440,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "0b0a46a4",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "234"
+       "82"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -556,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "8c1d72c4532bd509",
    "metadata": {},
    "outputs": [],
@@ -592,7 +591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "e2ebea6945193e07",
    "metadata": {},
    "outputs": [