k-means-algo.ipynb

{
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   "id": "a8849a6f-ee38-4cb6-a025-48ee236732e3",
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from IPython.display import display_html\n",
    "from sklearn import cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4ebdd39-acc0-4ded-bc36-c419b28215ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "def toy_dataset():\n",
    "    ratings=[['Lokesh',5,5,2,1],['Jyoti',4,5,3,2],['Bijay',4,4,4,3],['Sita',2,2,4,5],['Manish',1,2,3,4],['Ram',2,1,5,5]]\n",
    "    titles = ['user', 'Loot', 'Chino','Ghar', 'Aatma']\n",
    "    movies = pd.DataFrame(ratings,columns=titles)\n",
    "    display_html(movies)\n",
    "    return movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77ed737e-f5ba-4cb0-b31c-242f4b056ca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def k_means_learn(k,movies):\n",
    "    data = movies.drop('user',axis=1)\n",
    "    k_means = cluster.KMeans(n_clusters=2, max_iter=50, random_state=1)\n",
    "    k_means.fit(data)\n",
    "    labels = k_means.labels_\n",
    "    pd.DataFrame(labels, index=movies.user, columns=['Cluster ID'])\n",
    "    print(\"Learned cluster centroids for two clusters o and 1:\")\n",
    "    centroids = k_means.cluster_centers_\n",
    "    display_html(pd.DataFrame(centroids,columns=data.columns))\n",
    "    print(\"Now You can use cluster centroids to other users to determine their cluster assignments.\")\n",
    "    return(k_means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1b3f4e3d-7401-4a60-9ff0-89c3d0e394cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cluster_new_data(k_means, movies):\n",
    "    testData = np.array([[4,5,1,2],[3,2,4,4],[2,3,4,1],[3,2,3,3],[5,4,1,4]])\n",
    "    labels = k_means.predict(testData)\n",
    "    labels = labels.reshape(-1,1)\n",
    "    usernames = np.array(['Radhe', 'Riya','Pratik', 'Prativa', 'Shyam']).reshape(-1,1)\n",
    "    cols = movies.columns.tolist()\n",
    "    newusers = pd.DataFrame(np.concatenate((usernames, testData), axis=1),columns=cols)\n",
    "    cols.append('Assigned Cluster')\n",
    "    newusers_cluster = pd.DataFrame(np.concatenate((usernames, testData, labels), axis=1), columns = cols)\n",
    "    print(\"your New users (test data) are:\")\n",
    "    display_html(newusers)\n",
    "    print(\"New Users with their assigned cluster:\")\n",
    "    display_html(newusers_cluster)"
   ]
  },
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   "id": "a8a7c6fc-846e-41a5-975c-3d11b7f08677",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>Loot</th>\n",
       "      <th>Chino</th>\n",
       "      <th>Ghar</th>\n",
       "      <th>Aatma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lokesh</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jyoti</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bijay</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sita</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Manish</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ram</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learned cluster centroids for two clusters o and 1:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/anaconda3/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loot</th>\n",
       "      <th>Chino</th>\n",
       "      <th>Ghar</th>\n",
       "      <th>Aatma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.333333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now You can use cluster centroids to other users to determine their cluster assignments.\n",
      "your New users (test data) are:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/anaconda3/lib/python3.10/site-packages/sklearn/base.py:420: UserWarning: X does not have valid feature names, but KMeans was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>Loot</th>\n",
       "      <th>Chino</th>\n",
       "      <th>Ghar</th>\n",
       "      <th>Aatma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radhe</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Riya</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pratik</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prativa</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shyam</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
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     "text": [
      "New Users with their assigned cluster:\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>Loot</th>\n",
       "      <th>Chino</th>\n",
       "      <th>Ghar</th>\n",
       "      <th>Aatma</th>\n",
       "      <th>Assigned Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radhe</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Riya</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pratik</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prativa</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shyam</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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     "metadata": {},
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    }
   ],
   "source": [
    "def main():\n",
    "    k=2\n",
    "    movies = toy_dataset()\n",
    "    k_means=k_means_learn(k,movies)\n",
    "    cluster_new_data(k_means, movies)\n",
    "main()"
   ]
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