├── README.md
└── YouTube_Recommendations.ipynb


/README.md:
--------------------------------------------------------------------------------
 1 | # Customize-YouTube-Recommendations
 2 | 
 3 | To create an API Key
 4 | 1. Go to <a href = https://console.cloud.google.com/home/> Google Console</a> and enable <b>YouTube Data API v3</b>.
 5 | 2. To access enabled APIs create credentials : APIs & Services --> Credentials --> API Keys
 6 | 
 7 | How to run this?
 8 | 1. Download it and open through Jupyter Notebook.
 9 | 2. Simply click on <b>Open In Colab</b> button at the top of the .ipynb file and run through Google Colab.
10 | 


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/YouTube_Recommendations.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "YouTube Recommendations.ipynb",
  7 |       "provenance": [],
  8 |       "collapsed_sections": [],
  9 |       "authorship_tag": "ABX9TyNzEeNBF+Sp0UlXb0hXxhT/",
 10 |       "include_colab_link": true
 11 |     },
 12 |     "kernelspec": {
 13 |       "name": "python3",
 14 |       "display_name": "Python 3"
 15 |     },
 16 |     "language_info": {
 17 |       "name": "python"
 18 |     }
 19 |   },
 20 |   "cells": [
 21 |     {
 22 |       "cell_type": "markdown",
 23 |       "metadata": {
 24 |         "id": "view-in-github",
 25 |         "colab_type": "text"
 26 |       },
 27 |       "source": [
 28 |         "<a href=\"https://colab.research.google.com/github/Laya303/Customize-YouTube-Recommendations/blob/main/YouTube_Recommendations.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 29 |       ]
 30 |     },
 31 |     {
 32 |       "cell_type": "code",
 33 |       "metadata": {
 34 |         "id": "S7dR5LZikFjI"
 35 |       },
 36 |       "source": [
 37 |         "pip install google-api-python-client"
 38 |       ],
 39 |       "execution_count": null,
 40 |       "outputs": []
 41 |     },
 42 |     {
 43 |       "cell_type": "code",
 44 |       "metadata": {
 45 |         "id": "8cluwEjrs3Wk"
 46 |       },
 47 |       "source": [
 48 |         "import pandas as pd\n",
 49 |         "from datetime import datetime, timedelta\n",
 50 |         "from tabulate import tabulate\n",
 51 |         "from IPython.display import display, HTML\n",
 52 |         "from apiclient.discovery import build\n",
 53 |         "\n",
 54 |         "api_key = 'your_api_key_here'"
 55 |       ],
 56 |       "execution_count": 2,
 57 |       "outputs": []
 58 |     },
 59 |     {
 60 |       "cell_type": "code",
 61 |       "metadata": {
 62 |         "id": "0GUPRiQctNsW"
 63 |       },
 64 |       "source": [
 65 |         "def get_start_date_string(search_period_days):\n",
 66 |         "    #Returns string for start-date of search period\n",
 67 |         "    search_start_date = datetime.today() - timedelta(search_period_days)\n",
 68 |         "    date_string = datetime(year=search_start_date.year,month=search_start_date.month,\n",
 69 |         "                           day=search_start_date.day).strftime('%Y-%m-%dT%H:%M:%SZ')\n",
 70 |         "    return date_string"
 71 |       ],
 72 |       "execution_count": 3,
 73 |       "outputs": []
 74 |     },
 75 |     {
 76 |       "cell_type": "code",
 77 |       "metadata": {
 78 |         "id": "5AFBdYzMtdco"
 79 |       },
 80 |       "source": [
 81 |         "#Parsing key information about vidoes\n",
 82 |         "\n",
 83 |         "def find_title(item):\n",
 84 |         "    title = item['snippet']['title']\n",
 85 |         "    return title\n",
 86 |         "\n",
 87 |         "def find_video_url(item):\n",
 88 |         "    video_id = item['id']['videoId']\n",
 89 |         "    video_url = \"https://www.youtube.com/watch?v=\" + video_id\n",
 90 |         "    return video_url\n",
 91 |         "\n",
 92 |         "def find_viewcount(item, youtube):\n",
 93 |         "    video_id = item['id']['videoId']\n",
 94 |         "    video_statistics = youtube.videos().list(id=video_id,\n",
 95 |         "                                        part='statistics').execute()\n",
 96 |         "    viewcount = int(video_statistics['items'][0]['statistics']['viewCount'])\n",
 97 |         "    return viewcount\n",
 98 |         "\n",
 99 |         "def find_channel_id(item):\n",
100 |         "    channel_id = item['snippet']['channelId']\n",
101 |         "    return channel_id\n",
102 |         "\n",
103 |         "def find_channel_url(item):\n",
104 |         "    channel_id = item['snippet']['channelId']\n",
105 |         "    channel_url = \"https://www.youtube.com/channel/\" + channel_id\n",
106 |         "    return channel_url\n",
107 |         "\n",
108 |         "def find_channel_title(channel_id, youtube):\n",
109 |         "    channel_search = youtube.channels().list(id=channel_id,\n",
110 |         "                                            part='brandingSettings').execute()\n",
111 |         "    channel_name = channel_search['items'][0]\\\n",
112 |         "                                    ['brandingSettings']['channel']['title']\n",
113 |         "    return channel_name\n",
114 |         "\n",
115 |         "def find_num_subscribers(channel_id, youtube):\n",
116 |         "    subs_search = youtube.channels().list(id=channel_id,\n",
117 |         "                                            part='statistics').execute()\n",
118 |         "    if subs_search['items'][0]['statistics']['hiddenSubscriberCount']:\n",
119 |         "        num_subscribers = 1000000\n",
120 |         "    else:\n",
121 |         "        num_subscribers = int(subs_search['items'][0]\\\n",
122 |         "                                    ['statistics']['subscriberCount'])\n",
123 |         "    return num_subscribers\n",
124 |         "\n",
125 |         "def view_to_sub_ratio(viewcount, num_subscribers):\n",
126 |         "    if num_subscribers == 0:\n",
127 |         "        return 0\n",
128 |         "    else:\n",
129 |         "        ratio = viewcount / num_subscribers\n",
130 |         "        return ratio\n",
131 |         "\n",
132 |         "def age(item):\n",
133 |         "    when_published = item['snippet']['publishedAt']\n",
134 |         "    when_published_datetime_object = datetime.strptime(when_published,\n",
135 |         "                                                        '%Y-%m-%dT%H:%M:%SZ')\n",
136 |         "    today_date = datetime.today()\n",
137 |         "    days_since_published = int((today_date - when_published_datetime_object).days)\n",
138 |         "    if days_since_published == 0:\n",
139 |         "        days_since_published = 1\n",
140 |         "    return days_since_published\n",
141 |         "\n",
142 |         "def custom_score(viewcount, ratio, days_since_published):\n",
143 |         "    ratio = min(ratio, 5)\n",
144 |         "    score = (viewcount * ratio) / days_since_published\n",
145 |         "    return round(score)\n"
146 |       ],
147 |       "execution_count": 4,
148 |       "outputs": []
149 |     },
150 |     {
151 |       "cell_type": "code",
152 |       "metadata": {
153 |         "id": "lrSs3P1DupKt"
154 |       },
155 |       "source": [
156 |         "def find_videos(search_terms, api_key, views_threshold, uploaded_since):\n",
157 |         "\n",
158 |         "    # Initialise results dataframe\n",
159 |         "    dataframe = pd.DataFrame(columns=('Title', 'Link', 'Score',\n",
160 |         "                            'Views', 'Channel','Subscribers',\n",
161 |         "                            'View-Subscriber Ratio','Channel Link'))\n",
162 |         "\n",
163 |         "    # Run search and populate dataframe\n",
164 |         "    search_results, youtube_api = search_api(search_terms, api_key,\n",
165 |         "                                                        uploaded_since)\n",
166 |         "\n",
167 |         "    results_df = populate_dataframe(search_results, youtube_api, dataframe,\n",
168 |         "                                                        views_threshold)\n",
169 |         "\n",
170 |         "    return results_df\n",
171 |         "\n",
172 |         "def search_api(search_terms, api_key, uploaded_since):\n",
173 |         "    \n",
174 |         "    # Initialise API call\n",
175 |         "    youtube_api = build('youtube', 'v3', developerKey = api_key)\n",
176 |         "\n",
177 |         "    # Run search\n",
178 |         "    results = youtube_api.search().list(q=search_terms, part='snippet',\n",
179 |         "                                type='video', order='viewCount', maxResults=50,\n",
180 |         "                                publishedAfter=uploaded_since).execute()\n",
181 |         "\n",
182 |         "    return results, youtube_api\n",
183 |         "\n",
184 |         "\n",
185 |         "def populate_dataframe(results, youtube_api, df, views_threshold):\n",
186 |         "\n",
187 |         "    # Loop over search results and add key information to dataframe\n",
188 |         "    i = 1\n",
189 |         "    for item in results['items']:\n",
190 |         "        viewcount = find_viewcount(item, youtube_api)\n",
191 |         "        if viewcount > views_threshold:\n",
192 |         "            title = find_title(item)\n",
193 |         "            video_url = find_video_url(item)\n",
194 |         "            channel_url = find_channel_url(item)\n",
195 |         "            channel_id = find_channel_id(item)\n",
196 |         "            channel_name = find_channel_title(channel_id, youtube_api)\n",
197 |         "            num_subs = find_num_subscribers(channel_id, youtube_api)\n",
198 |         "            ratio = view_to_sub_ratio(viewcount, num_subs)\n",
199 |         "            days_since_published = age(item)\n",
200 |         "            score = custom_score(viewcount, ratio, days_since_published)\n",
201 |         "            df.loc[i] = [title, video_url, score, viewcount, channel_name,\\\n",
202 |         "                                    num_subs, format(ratio, '.1f'), channel_url]\n",
203 |         "        i += 1\n",
204 |         "    return df\n"
205 |       ],
206 |       "execution_count": 5,
207 |       "outputs": []
208 |     },
209 |     {
210 |       "cell_type": "code",
211 |       "metadata": {
212 |         "id": "09DrmEs9s_rN"
213 |       },
214 |       "source": [
215 |         "def search_each_term(search_terms, api_key, uploaded_since=get_start_date_string(7),\n",
216 |         "                        views_threshold=5000, num_to_print=5):\n",
217 |         "    \n",
218 |         "    #Uses search term to execute API calls\n",
219 |         "    if type(search_terms) == str:\n",
220 |         "        search_terms = [search_terms]\n",
221 |         "\n",
222 |         "    \n",
223 |         "    for index, search_term in enumerate(search_terms):\n",
224 |         "        df = find_videos(search_terms[index], api_key, views_threshold=views_threshold,\n",
225 |         "                         uploaded_since = uploaded_since)\n",
226 |         "        df = df.sort_values(['Score'], ascending=[0])\n",
227 |         "    \n",
228 |         "    print(\"\\n\\nHi Sreelaya\\nHere are the videos you should watch this week\\n\\n\")\n",
229 |         "    print_top_videos(df, num_to_print)                                          #Prints top 5 vidoes\n",
230 |         "    \n",
231 |         "    print(\"\\n\\nThese are all the results fetched...\\n\")\n",
232 |         "    pd.set_option('max_colwidth', 400)\n",
233 |         "\n",
234 |         "    #Modifying Title and Channel columns as hyperlinks\n",
235 |         "    df['Title'] = '<a href=' + df['Link'] + '><div>' + df['Title'] + '</div></a>'\n",
236 |         "    df['Channel'] = '<a href=' + df['Channel Link'] + '><div>' + df['Channel'] + '</div></a>' \n",
237 |         "    final_df = df.drop(['Link', 'Channel Link'], axis=1)\n",
238 |         "\n",
239 |         "\n",
240 |         "    dfStyler = final_df.style.set_properties(subset=['Title'],**{'text-align': 'left'})\n",
241 |         "    dfStyler = final_df.style.set_properties(subset=['Score', 'Views', 'Channel','Subscribers',\n",
242 |         "                            'View-Subscriber Ratio'],**{'text-align': 'center'})\n",
243 |         "    dfStyler.set_table_styles([dict(selector='th', props=[('text-align', 'center')])]).hide_index()\n",
244 |         "    \n",
245 |         "    display(HTML(dfStyler.render()))\n",
246 |         "\n",
247 |         "def print_top_videos(df, num_to_print):\n",
248 |         "    if len(df) < num_to_print:\n",
249 |         "        num_to_print = len(df)\n",
250 |         "    if num_to_print == 0:\n",
251 |         "        print(\"No video results found\")\n",
252 |         "    else:\n",
253 |         "        for i in range(num_to_print):\n",
254 |         "            video = df.iloc[i]\n",
255 |         "            title = video['Title']\n",
256 |         "            link = video['Link']\n",
257 |         "            print(\"#{}\\nTitle: '{}' \\nURL: {} \\n\"\\\n",
258 |         "                                        .format(i+1, title, link))\n",
259 |         "\n"
260 |       ],
261 |       "execution_count": 6,
262 |       "outputs": []
263 |     },
264 |     {
265 |       "cell_type": "code",
266 |       "metadata": {
267 |         "colab": {
268 |           "base_uri": "https://localhost:8080/",
269 |           "height": 697
270 |         },
271 |         "id": "Ar5U_OxQuXBd",
272 |         "outputId": "5feeea7e-8771-48d6-f1c2-c70cae72177f"
273 |       },
274 |       "source": [
275 |         "search_each_term(\"Machine Learning\", api_key, num_to_print=5)                   #search_each_term(\"Data Science\", api_key, '2021-01-11T00:00:00Z' )\n",
276 |         " "
277 |       ],
278 |       "execution_count": 7,
279 |       "outputs": [
280 |         {
281 |           "output_type": "stream",
282 |           "text": [
283 |             "\n",
284 |             "\n",
285 |             "Hi Sreelaya\n",
286 |             "Here are the videos you should watch this week\n",
287 |             "\n",
288 |             "\n",
289 |             "#1\n",
290 |             "Title: 'Linear Regression with Scikit Learn | Lesson 1 of 6 | Machine Learning with Python: Zero to GBMs' \n",
291 |             "URL: https://www.youtube.com/watch?v=CVszSgTWODE \n",
292 |             "\n",
293 |             "#2\n",
294 |             "Title: 'XCiT: Cross-Covariance Image Transformers (Facebook AI Machine Learning Research Paper Explained)' \n",
295 |             "URL: https://www.youtube.com/watch?v=g08NkNWmZTA \n",
296 |             "\n",
297 |             "#3\n",
298 |             "Title: '#55 Self-Supervised Vision Models (Dr. Ishan Misra - FAIR).' \n",
299 |             "URL: https://www.youtube.com/watch?v=EXJmodhu4_4 \n",
300 |             "\n",
301 |             "#4\n",
302 |             "Title: 'Künstliche Intelligenz: Wie Machine Learning unsere Gesellschaft verändert' \n",
303 |             "URL: https://www.youtube.com/watch?v=xhOeuqL2Pro \n",
304 |             "\n",
305 |             "#5\n",
306 |             "Title: 'Sign Language Detection using ACTION RECOGNITION with Python | LSTM Deep Learning Model' \n",
307 |             "URL: https://www.youtube.com/watch?v=doDUihpj6ro \n",
308 |             "\n",
309 |             "\n",
310 |             "\n",
311 |             "These are all the results fetched...\n",
312 |             "\n"
313 |           ],
314 |           "name": "stdout"
315 |         },
316 |         {
317 |           "output_type": "display_data",
318 |           "data": {
319 |             "text/html": [
320 |               "<style  type=\"text/css\" >\n",
321 |               "    #T_15a0908e_d5c2_11eb_ba54_0242ac1c0002 th {\n",
322 |               "          text-align: center;\n",
323 |               "    }#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row0_col1,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row0_col2,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row0_col3,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row0_col4,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row0_col5,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row1_col1,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row1_col2,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row1_col3,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row1_col4,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row1_col5,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row2_col1,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row2_col2,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row2_col3,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row2_col4,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row2_col5,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row3_col1,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row3_col2,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row3_col3,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row3_col4,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row3_col5,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row4_col1,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row4_col2,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row4_col3,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row4_col4,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row4_col5,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row5_col1,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row5_col2,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row5_col3,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row5_col4,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row5_col5,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row6_col1,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row6_col2,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row6_col3,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row6_col4,#T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row6_col5{\n",
324 |               "            text-align:  center;\n",
325 |               "        }</style><table id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >Title</th>        <th class=\"col_heading level0 col1\" >Score</th>        <th class=\"col_heading level0 col2\" >Views</th>        <th class=\"col_heading level0 col3\" >Channel</th>        <th class=\"col_heading level0 col4\" >Subscribers</th>        <th class=\"col_heading level0 col5\" >View-Subscriber Ratio</th>    </tr></thead><tbody>\n",
326 |               "                <tr>\n",
327 |               "                                <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row0_col0\" class=\"data row0 col0\" ><a href=https://www.youtube.com/watch?v=CVszSgTWODE><div>Linear Regression with Scikit Learn | Lesson 1 of 6 | Machine Learning with Python: Zero to GBMs</div></a></td>\n",
328 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row0_col1\" class=\"data row0 col1\" >1595</td>\n",
329 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row0_col2\" class=\"data row0 col2\" >10752</td>\n",
330 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row0_col3\" class=\"data row0 col3\" ><a href=https://www.youtube.com/channel/UCmKaoNn0OvxVAe7f_8sXYNQ><div>Jovian</div></a></td>\n",
331 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row0_col4\" class=\"data row0 col4\" >14500</td>\n",
332 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.7</td>\n",
333 |               "            </tr>\n",
334 |               "            <tr>\n",
335 |               "                                <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row1_col0\" class=\"data row1 col0\" ><a href=https://www.youtube.com/watch?v=g08NkNWmZTA><div>XCiT: Cross-Covariance Image Transformers (Facebook AI Machine Learning Research Paper Explained)</div></a></td>\n",
336 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row1_col1\" class=\"data row1 col1\" >429</td>\n",
337 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row1_col2\" class=\"data row1 col2\" >8865</td>\n",
338 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row1_col3\" class=\"data row1 col3\" ><a href=https://www.youtube.com/channel/UCZHmQk67mSJgfCCTn7xBfew><div>Yannic Kilcher</div></a></td>\n",
339 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row1_col4\" class=\"data row1 col4\" >91500</td>\n",
340 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.1</td>\n",
341 |               "            </tr>\n",
342 |               "            <tr>\n",
343 |               "                                <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row2_col0\" class=\"data row2 col0\" ><a href=https://www.youtube.com/watch?v=EXJmodhu4_4><div>#55 Self-Supervised Vision Models (Dr. Ishan Misra - FAIR).</div></a></td>\n",
344 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row2_col1\" class=\"data row2 col1\" >400</td>\n",
345 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row2_col2\" class=\"data row2 col2\" >5153</td>\n",
346 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row2_col3\" class=\"data row2 col3\" ><a href=https://www.youtube.com/channel/UCMLtBahI5DMrt0NPvDSoIRQ><div>Machine Learning Street Talk</div></a></td>\n",
347 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row2_col4\" class=\"data row2 col4\" >16600</td>\n",
348 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.3</td>\n",
349 |               "            </tr>\n",
350 |               "            <tr>\n",
351 |               "                                <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row3_col0\" class=\"data row3 col0\" ><a href=https://www.youtube.com/watch?v=xhOeuqL2Pro><div>Künstliche Intelligenz: Wie Machine Learning unsere Gesellschaft verändert</div></a></td>\n",
352 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row3_col1\" class=\"data row3 col1\" >304</td>\n",
353 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row3_col2\" class=\"data row3 col2\" >14721</td>\n",
354 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row3_col3\" class=\"data row3 col3\" ><a href=https://www.youtube.com/channel/UC9Iq-yi4q3lsnSEXltzpqcQ><div>Florian Homm</div></a></td>\n",
355 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row3_col4\" class=\"data row3 col4\" >238000</td>\n",
356 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.1</td>\n",
357 |               "            </tr>\n",
358 |               "            <tr>\n",
359 |               "                                <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row4_col0\" class=\"data row4 col0\" ><a href=https://www.youtube.com/watch?v=doDUihpj6ro><div>Sign Language Detection using ACTION RECOGNITION with Python | LSTM Deep Learning Model</div></a></td>\n",
360 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row4_col1\" class=\"data row4 col1\" >208</td>\n",
361 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row4_col2\" class=\"data row4 col2\" >5380</td>\n",
362 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row4_col3\" class=\"data row4 col3\" ><a href=https://www.youtube.com/channel/UCHXa4OpASJEwrHrLeIzw7Yg><div>Nicholas Renotte</div></a></td>\n",
363 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row4_col4\" class=\"data row4 col4\" >23200</td>\n",
364 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.2</td>\n",
365 |               "            </tr>\n",
366 |               "            <tr>\n",
367 |               "                                <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row5_col0\" class=\"data row5 col0\" ><a href=https://www.youtube.com/watch?v=skGjo9YKfEo><div>White Box Vs Black Box Models In Machine Learning- Data Science Interview Question</div></a></td>\n",
368 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row5_col1\" class=\"data row5 col1\" >47</td>\n",
369 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row5_col2\" class=\"data row5 col2\" >8469</td>\n",
370 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row5_col3\" class=\"data row5 col3\" ><a href=https://www.youtube.com/channel/UCNU_lfiiWBdtULKOw6X0Dig><div>Krish Naik</div></a></td>\n",
371 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row5_col4\" class=\"data row5 col4\" >384000</td>\n",
372 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.0</td>\n",
373 |               "            </tr>\n",
374 |               "            <tr>\n",
375 |               "                                <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row6_col0\" class=\"data row6 col0\" ><a href=https://www.youtube.com/watch?v=lGNJIDFDj1A><div>Deep Learning with Python Tutorial | Deep Learning Training | Edureka | DL Rewind - 1</div></a></td>\n",
376 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row6_col1\" class=\"data row6 col1\" >5</td>\n",
377 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row6_col2\" class=\"data row6 col2\" >5252</td>\n",
378 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row6_col3\" class=\"data row6 col3\" ><a href=https://www.youtube.com/channel/UCkw4JCwteGrDHIsyIIKo4tQ><div>edureka!</div></a></td>\n",
379 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row6_col4\" class=\"data row6 col4\" >2710000</td>\n",
380 |               "                        <td id=\"T_15a0908e_d5c2_11eb_ba54_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.0</td>\n",
381 |               "            </tr>\n",
382 |               "    </tbody></table>"
383 |             ],
384 |             "text/plain": [
385 |               "<IPython.core.display.HTML object>"
386 |             ]
387 |           },
388 |           "metadata": {
389 |             "tags": []
390 |           }
391 |         }
392 |       ]
393 |     }
394 |   ]
395 | }


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