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Commit fb6a0fff authored by Nirmal M.D.S's avatar Nirmal M.D.S
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pIPRzpKBdHYW",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8e883495-ed72-4704-ee9d-f9c5a6465ebf"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EQWrFYcidIGL",
"outputId": "6035b7de-c51d-4374-b143-01fa48ba3934"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/drive/My Drive/PP1 Practice\n"
]
}
],
"source": [
"%cd \"/content/drive/My Drive/PP1 Practice/\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7cIHFinPt0KH"
},
"outputs": [],
"source": [
"#import packages\n",
"import pandas as pd\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "rl4NpPiKt4Y-",
"outputId": "d2a60f7e-080a-456e-bf64-07586069b272"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
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" ProjectName TaskDescription \\\n",
"0 E-commerce Website Implement a user registration and login system... \n",
"1 Mobile App Development Develop a push notification feature for the mo... \n",
"2 Data Analytics Platform Build a data visualization module that present... \n",
"3 CRM System Upgrade Enhance the existing customer relationship man... \n",
"4 Bug Tracking Tool Create a web-based bug tracking tool that allo... \n",
"\n",
" Level \n",
"0 Low \n",
"1 Low \n",
"2 Low \n",
"3 Low \n",
"4 Low "
],
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" <tr>\n",
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" <td>Data Analytics Platform</td>\n",
" <td>Build a data visualization module that present...</td>\n",
" <td>Low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>CRM System Upgrade</td>\n",
" <td>Enhance the existing customer relationship man...</td>\n",
" <td>Low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Bug Tracking Tool</td>\n",
" <td>Create a web-based bug tracking tool that allo...</td>\n",
" <td>Low</td>\n",
" </tr>\n",
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"\n",
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" .colab-df-container {\n",
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"\n",
" .colab-df-convert {\n",
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"\n",
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" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
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"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
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" [theme=dark] .colab-df-convert {\n",
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" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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" document.querySelector('#df-65fab58d-70aa-44dc-b052-21323b44bec0 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-65fab58d-70aa-44dc-b052-21323b44bec0');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
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"\n",
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"\n",
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" width=\"24px\">\n",
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" </g>\n",
"</svg>\n",
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"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
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"\n",
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"\n",
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" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
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"\n",
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" background-color: var(--disabled-bg-color);\n",
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"\n",
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" border-bottom-color: var(--fill-color);\n",
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"\n",
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" }\n",
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" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-1451cc01-be01-4fc6-b22d-3ae6b9dcce52 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 5
}
],
"source": [
"#import data\n",
"df = pd.read_csv(\"/content/drive/MyDrive/PP1 Practice/TaskDescCopy3.csv\")\n",
"\n",
"#inspect dataset\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 210
},
"id": "N7VfsALBt7E1",
"outputId": "bf9728f0-4cb3-4ab1-c280-35d27c427a98"
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"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" ProjectName \\\n",
" count unique top freq \n",
"Level \n",
"High 54 47 Recommendation System Evaluation 2 \n",
"Low 94 82 Progressive Web Application 2 \n",
"\n",
" TaskDescription \\\n",
" count unique \n",
"Level \n",
"High 54 47 \n",
"Low 94 84 \n",
"\n",
" \n",
" top freq \n",
"Level \n",
"High Evaluate and fine-tune the performance of a re... 2 \n",
"Low Develop a custom data visualization library fo... 2 "
],
"text/html": [
"\n",
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" <th>unique</th>\n",
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" <tr>\n",
" <th>Level</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>High</th>\n",
" <td>54</td>\n",
" <td>47</td>\n",
" <td>Recommendation System Evaluation</td>\n",
" <td>2</td>\n",
" <td>54</td>\n",
" <td>47</td>\n",
" <td>Evaluate and fine-tune the performance of a re...</td>\n",
" <td>2</td>\n",
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" <td>94</td>\n",
" <td>82</td>\n",
" <td>Progressive Web Application</td>\n",
" <td>2</td>\n",
" <td>94</td>\n",
" <td>84</td>\n",
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" <td>2</td>\n",
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"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
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"\n",
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" </g>\n",
"</svg>\n",
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"\n",
"<style>\n",
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" }\n",
"\n",
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" display: none;\n",
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"\n",
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"\n",
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" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
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" border-bottom-color: var(--fill-color);\n",
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"metadata": {},
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"source": [
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "N55IenEMBV85"
},
"outputs": [],
"source": [
"df['high'] = df['Level'].apply(lambda x: 1 if x == 'High' else 0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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" ProjectName \\\n",
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"3 CRM System Upgrade \n",
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".. ... \n",
"143 Gamification Feature \n",
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"147 A/B Testing Platform \n",
"\n",
" TaskDescription Level high \n",
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"1 Develop a push notification feature for the mo... Low 0 \n",
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"4 Create a web-based bug tracking tool that allo... Low 0 \n",
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"143 Implement gamification features to enhance use... Low 0 \n",
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},
"metadata": {},
"execution_count": 8
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9XVubeEMDwJN"
},
"outputs": [],
"source": [
"#train test split\n",
"x_train, x_test, y_train, y_test = train_test_split(df.TaskDescription, df.high, test_size=0.2)"
]
},
{
"cell_type": "code",
"source": [
"x_train"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XkomH8f2215K",
"outputId": "e56869db-7caa-4fca-d370-38fa26c1f2cd"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"127 Optimize cloud costs by analyzing resource usa...\n",
"111 Design and develop an A/B testing platform for...\n",
"146 Conduct a data privacy impact assessment (DPIA...\n",
"90 Implement a speech recognition system to conve...\n",
"45 Develop an inventory management system to trac...\n",
" ... \n",
"18 Redesign the company website to make it more m...\n",
"123 Implement voice biometrics for user authentica...\n",
"129 Perform sales funnel analysis to identify bott...\n",
"3 Enhance the existing customer relationship man...\n",
"39 Develop a chatbot feature for customer support...\n",
"Name: TaskDescription, Length: 118, dtype: object"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"x_train.describe()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hAaehk3024w2",
"outputId": "694b3746-a488-4081-b7ae-f3290e300772"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"count 118\n",
"unique 109\n",
"top Design and implement a data lake architecture ...\n",
"freq 2\n",
"Name: TaskDescription, dtype: object"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"#find the word count and storing data in a numerical matrix\n",
"cv = CountVectorizer()\n",
"x_train_count = cv.fit_transform(x_train.values) #turning the descriptions in train dataset into a matrix\n"
],
"metadata": {
"id": "_td_dr0s3CBN"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"x_train_count"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "S5ufUn1-4dpf",
"outputId": "898b7896-84b8-4aec-843b-01fd75a40b37"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<118x798 sparse matrix of type '<class 'numpy.int64'>'\n",
"\twith 2333 stored elements in Compressed Sparse Row format>"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"x_train_count.toarray()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "n5WzCLqX4h9M",
"outputId": "a76ef34f-2172-4db1-9894-7c1e9e579337"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" ...,\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0]])"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"source": [
"#train model\n",
"model = MultinomialNB()\n",
"model.fit(x_train_count, y_train) #training the model using our converted x train value set and y_train data"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
},
"id": "OejJluIB4pO8",
"outputId": "f7ddef8e-ff54-4716-e005-e204644b0491"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"MultinomialNB()"
],
"text/html": [
"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"source": [
"#validation\n",
"task1 = [\"Machine learning pplatform to predict Cardiac Diseases\"]\n",
"task1_count = cv.transform(task1) #Using CountVectorizer Function\n",
"model.predict(task1_count)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5w0z9Rt47nGS",
"outputId": "a2b3d4aa-70dc-4ba6-a90e-7d08de4f9d34"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1])"
]
},
"metadata": {},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"source": [
"#validation\n",
"task2 = [\"Refactor a code\"]\n",
"task2_count = cv.transform(task2) #Using CountVectorizer Function conversion is done\n",
"model.predict(task2_count)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bzZMZ3G58Oco",
"outputId": "e4dbf420-4d6b-4ff8-b67a-4f406b0def0d"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0])"
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"source": [
"#testing the model\n",
"x_test_count = cv.transform(x_test) #converting test data to matrix form\n",
"model.score(x_test_count, y_test) #testing against the y labels of the testing data"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8rEHDqXF9BoP",
"outputId": "4b28b1d8-ff18-4eea-ab00-27719e8d8439"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.8333333333333334"
]
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"source": [
"#make predictions using testing data\n",
"y_pred = model.predict(x_test_count)"
],
"metadata": {
"id": "qWGXwN2X9y-W"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#Evaluate the performance of the classifier\n",
"print(classification_report(y_test,y_pred))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kPbI2biv-g-F",
"outputId": "1920de39-9ab0-4035-8b54-25eec764d369"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.89 0.84 0.86 19\n",
" 1 0.75 0.82 0.78 11\n",
"\n",
" accuracy 0.83 30\n",
" macro avg 0.82 0.83 0.82 30\n",
"weighted avg 0.84 0.83 0.83 30\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Calculate the accuracy of the model\n",
"accuracy = accuracy_score(y_test, y_pred)\n",
"print(\"Accuracy:\", accuracy)"
],
"metadata": {
"id": "thbdaDaA-tsF",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f6f373c6-0057-43eb-e1f4-ddaaf1860675"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy: 0.8333333333333334\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import f1_score\n",
"f1 = f1_score(y_test, y_pred)\n",
"print(\"F1 score:\", f1)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "favBrnZWJnLt",
"outputId": "a22dc8c6-fdab-4e8a-867a-67e8107983cf"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"F1 score: 0.7826086956521738\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import recall_score\n",
"recall = recall_score(y_test, y_pred)\n",
"print(\"Recall:\", recall)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Scr5KlNdJ5xw",
"outputId": "cfedfba5-a751-4876-c201-0202bff54196"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Recall: 0.8181818181818182\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import precision_score\n",
"precision = precision_score(y_test, y_pred)\n",
"print(\"Precision:\", precision)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5Th4pX7NKqF2",
"outputId": "21fb8a68-6405-438e-cafc-4536b7ff4158"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Precision: 0.75\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import accuracy_score,confusion_matrix\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
],
"metadata": {
"id": "o7clj6nahd4E"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"cm=confusion_matrix(y_test,y_pred)\n",
"fig=plt.figure(figsize=(12,8))\n",
"sns.heatmap(\n",
" cm,\n",
" annot=True,\n",
")\n",
"plt.title(\"Confusion Matrix for Naive Bayes Classifier\")\n",
"cm"
],
"metadata": {
"id": "mWEuzWnZLiPN",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 691
},
"outputId": "4723b7fb-4caf-427c-8959-54dfe5f8d9a2"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[16, 3],\n",
" [ 2, 9]])"
]
},
"metadata": {},
"execution_count": 31
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x800 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "DkNbZ5zbhLBc"
},
"execution_count": null,
"outputs": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
\ No newline at end of file
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