{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\Asus\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "import pandas as pd \n", "import numpy as np \n", "import seaborn as sns \n", "import matplotlib as plt\n", "import gradio as gr \n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import confusion_matrix,accuracy_score,precision_score,recall_score,f1_score\n", "from sklearn.ensemble import RandomForestClassifier\n", "data = pd.read_csv(\"../career-recommendation-system/Dataset/mldata.csv\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6901, 20)\n" ] } ], "source": [ "print(data.shape)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 6901 entries, 0 to 6900\n", "Data columns (total 20 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Logical quotient rating 6901 non-null int64 \n", " 1 hackathons 6901 non-null int64 \n", " 2 coding skills rating 6901 non-null int64 \n", " 3 public speaking points 6901 non-null int64 \n", " 4 self-learning capability? 6901 non-null object\n", " 5 Extra-courses did 6901 non-null object\n", " 6 certifications 6901 non-null object\n", " 7 workshops 6901 non-null object\n", " 8 reading and writing skills 6901 non-null object\n", " 9 memory capability score 6901 non-null object\n", " 10 Interested subjects 6901 non-null object\n", " 11 interested career area 6901 non-null object\n", " 12 Type of company want to settle in? 6901 non-null object\n", " 13 Taken inputs from seniors or elders 6901 non-null object\n", " 14 Interested Type of Books 6901 non-null object\n", " 15 Management or Technical 6901 non-null object\n", " 16 hard/smart worker 6901 non-null object\n", " 17 worked in teams ever? 6901 non-null object\n", " 18 Introvert 6901 non-null object\n", " 19 Suggested Job Role 6901 non-null object\n", "dtypes: int64(4), object(16)\n", "memory usage: 1.1+ MB\n" ] } ], "source": [ "data.info()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "List of numerical features: \n", " ['Logical quotient rating', 'hackathons', 'coding skills rating', 'public speaking points']\n", "List of categorical features: \n", " ['self-learning capability?', 'Extra-courses did', 'certifications', 'workshops', 'reading and writing skills', 'memory capability score', 'Interested subjects', 'interested career area ', 'Type of company want to settle in?', 'Taken inputs from seniors or elders', 'Interested Type of Books', 'Management or Technical', 'hard/smart worker', 'worked in teams ever?', 'Introvert', 'Suggested Job Role']\n" ] } ], "source": [ "print(\"List of numerical features: \\n\", data.select_dtypes(include=\"int\").columns.tolist()) \n", "print(\"List of categorical features: \\n\", data.select_dtypes(include=\"object\").columns.tolist())" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Logical quotient rating 0\n", "hackathons 0\n", "coding skills rating 0\n", "public speaking points 0\n", "self-learning capability? 0\n", "Extra-courses did 0\n", "certifications 0\n", "workshops 0\n", "reading and writing skills 0\n", "memory capability score 0\n", "Interested subjects 0\n", "interested career area 0\n", "Type of company want to settle in? 0\n", "Taken inputs from seniors or elders 0\n", "Interested Type of Books 0\n", "Management or Technical 0\n", "hard/smart worker 0\n", "worked in teams ever? 0\n", "Introvert 0\n", "Suggested Job Role 0\n", "dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "self-learning capability?\n", "yes 3496\n", "no 3405\n", "Name: count, dtype: int64\n", "\n", "Extra-courses did\n", "no 3529\n", "yes 3372\n", "Name: count, dtype: int64\n", "\n", "certifications\n", "r programming 803\n", "information security 785\n", "shell programming 783\n", "machine learning 783\n", "full stack 768\n", "hadoop 764\n", "python 756\n", "distro making 740\n", "app development 719\n", "Name: count, dtype: int64\n", "\n", "workshops\n", "database security 897\n", "system designing 891\n", "web technologies 891\n", "hacking 867\n", "testing 852\n", "data science 842\n", "game development 831\n", "cloud computing 830\n", "Name: count, dtype: int64\n", "\n", "reading and writing skills\n", "excellent 2328\n", "medium 2315\n", "poor 2258\n", "Name: count, dtype: int64\n", "\n", "memory capability score\n", "medium 2317\n", "excellent 2303\n", "poor 2281\n", "Name: count, dtype: int64\n", "\n", "Interested subjects\n", "Software Engineering 731\n", "IOT 722\n", "cloud computing 721\n", "programming 716\n", "networks 713\n", "Computer Architecture 703\n", "data engineering 672\n", "hacking 663\n", "Management 644\n", "parallel computing 616\n", "Name: count, dtype: int64\n", "\n", "interested career area \n", "system developer 1178\n", "security 1177\n", "Business process analyst 1154\n", "developer 1145\n", "testing 1128\n", "cloud computing 1119\n", "Name: count, dtype: int64\n", "\n", "Type of company want to settle in?\n", "Service Based 725\n", "Web Services 719\n", "BPA 711\n", "Testing and Maintainance Services 698\n", "Product based 695\n", "Finance 694\n", "Cloud Services 692\n", "product development 669\n", "Sales and Marketing 658\n", "SAaS services 640\n", "Name: count, dtype: int64\n", "\n", "Taken inputs from seniors or elders\n", "yes 3501\n", "no 3400\n", "Name: count, dtype: int64\n", "\n", "Interested Type of Books\n", "Guide 405\n", "Health 401\n", "Self help 377\n", "Horror 377\n", "Biographies 219\n", "Science fiction 218\n", "Satire 212\n", "Childrens 212\n", "Autobiographies 210\n", "Prayer books 207\n", "Fantasy 205\n", "Journals 203\n", "Trilogy 203\n", "Anthology 202\n", "Encyclopedias 201\n", "Drama 201\n", "Mystery 200\n", "History 199\n", "Science 198\n", "Dictionaries 198\n", "Diaries 197\n", "Religion-Spirituality 197\n", "Action and Adventure 193\n", "Poetry 193\n", "Cookbooks 186\n", "Comics 186\n", "Art 186\n", "Travel 186\n", "Series 180\n", "Math 176\n", "Romance 173\n", "Name: count, dtype: int64\n", "\n", "Management or Technical\n", "Management 3461\n", "Technical 3440\n", "Name: count, dtype: int64\n", "\n", "hard/smart worker\n", "smart worker 3523\n", "hard worker 3378\n", "Name: count, dtype: int64\n", "\n", "worked in teams ever?\n", "no 3470\n", "yes 3431\n", "Name: count, dtype: int64\n", "\n", "Introvert\n", "yes 3544\n", "no 3357\n", "Name: count, dtype: int64\n", "\n", "Suggested Job Role\n", "Network Security Engineer 630\n", "Software Engineer 590\n", "UX Designer 589\n", "Software Developer 587\n", "Database Developer 581\n", "Software Quality Assurance (QA) / Testing 571\n", "Web Developer 570\n", "CRM Technical Developer 567\n", "Technical Support 565\n", "Systems Security Administrator 562\n", "Applications Developer 551\n", "Mobile Applications Developer 538\n", "Name: count, dtype: int64\n", "\n" ] } ], "source": [ "categorical_cols = data[['self-learning capability?', 'Extra-courses did', 'certifications', 'workshops', 'reading and writing skills', 'memory capability score', 'Interested subjects', 'interested career area ', 'Type of company want to settle in?', 'Taken inputs from seniors or elders', 'Interested Type of Books', 'Management or Technical', 'hard/smart worker', 'worked in teams ever?', 'Introvert', 'Suggested Job Role']]\n", "numerical_cols = data[['Logical quotient rating', 'hackathons', 'coding skills rating', 'public speaking points']]\n", "\n", "for i in categorical_cols:\n", " print(data[i].value_counts(), end=\"\\n\\n\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logical quotient rating\n", "6 799\n", "9 784\n", "2 782\n", "5 773\n", "3 772\n", "4 759\n", "1 756\n", "7 752\n", "8 724\n", "Name: count, dtype: int64\n", "\n", "hackathons\n", "5 1033\n", "2 1026\n", "0 1010\n", "6 989\n", "3 966\n", "1 952\n", "4 925\n", "Name: count, dtype: int64\n", "\n", "coding skills rating\n", "4 787\n", "5 777\n", "2 776\n", "6 774\n", "8 767\n", "7 766\n", "9 761\n", "3 755\n", "1 738\n", "Name: count, dtype: int64\n", "\n", "public speaking points\n", "7 807\n", "1 799\n", "8 777\n", "2 770\n", "3 766\n", "4 760\n", "9 758\n", "6 740\n", "5 724\n", "Name: count, dtype: int64\n", "\n" ] } ], "source": [ "for j in numerical_cols:\n", " print(data[j].value_counts(), end=\"\\n\\n\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "[(0.6313725490196078, 0.788235294117647, 0.9568627450980393),\n", " (1.0, 0.7058823529411765, 0.5098039215686274),\n", " (0.5529411764705883, 0.8980392156862745, 0.6313725490196078),\n", " (1.0, 0.6235294117647059, 0.6078431372549019),\n", " (0.8156862745098039, 0.7333333333333333, 1.0),\n", " (0.8705882352941177, 0.7333333333333333, 0.6078431372549019),\n", " (0.9803921568627451, 0.6901960784313725, 0.8941176470588236),\n", " (0.8117647058823529, 0.8117647058823529, 0.8117647058823529),\n", " (1.0, 0.996078431372549, 0.6392156862745098),\n", " (0.7254901960784313, 0.9490196078431372, 0.9411764705882353)]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sns.set_theme(style=\"darkgrid\")\n", "Palette = sns.color_palette(\"pastel\")\n", "Palette\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_21412\\4210140923.py:3: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.countplot( data[\"Suggested Job Role\"], palette=sns.color_palette(\"pastel\"))\n", "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_21412\\4210140923.py:3: UserWarning: \n", "The palette list has fewer values (10) than needed (12) and will cycle, which may produce an uninterpretable plot.\n", " sns.countplot( data[\"Suggested Job Role\"], palette=sns.color_palette(\"pastel\"))\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We check for balancy for the label attribute, since this recommendation system \n", "# is similar classification model, therefore, it is a must to balance the label attribute\n", "sns.countplot( data[\"Suggested Job Role\"], palette=sns.color_palette(\"pastel\"))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "corr = data[['Logical quotient rating', 'hackathons', 'coding skills rating', 'public speaking points']].corr() \n", "sns.heatmap(corr,square=True,annot=True,linewidth = .2,center=2,cmap=Palette)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Logical quotient rating hackathons coding skills rating \\\n", "0 5 0 6 \n", "1 7 6 4 \n", "2 2 3 9 \n", "3 2 6 3 \n", "4 2 0 3 \n", "... ... ... ... \n", "6896 7 5 6 \n", "6897 6 5 1 \n", "6898 5 1 6 \n", "6899 1 6 4 \n", "6900 5 6 2 \n", "\n", " public speaking points \n", "0 2 \n", "1 3 \n", "2 1 \n", "3 5 \n", "4 4 \n", "... ... \n", "6896 2 \n", "6897 8 \n", "6898 7 \n", "6899 6 \n", "6900 5 \n", "\n", "[6901 rows x 4 columns]\n" ] } ], "source": [ "print(data.T.head(4).T)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['self-learning capability?', 'Extra-courses did', 'certifications', 'workshops', 'reading and writing skills', 'memory capability score', 'Interested subjects', 'interested career area ', 'Type of company want to settle in?', 'Taken inputs from seniors or elders', 'Interested Type of Books', 'Management or Technical', 'hard/smart worker', 'worked in teams ever?', 'Introvert', 'Suggested Job Role']\n", " self-learning capability? Extra-courses did certifications \\\n", "0 yes no information security \n", "1 no yes shell programming \n", "2 no yes information security \n", "3 no yes r programming \n", "4 yes no distro making \n", "... ... ... ... \n", "6896 yes no shell programming \n", "6897 no no machine learning \n", "6898 yes no distro making \n", "6899 no no app development \n", "6900 no yes information security \n", "\n", " workshops reading and writing skills memory capability score \\\n", "0 testing poor poor \n", "1 testing excellent medium \n", "2 testing excellent poor \n", "3 database security excellent poor \n", "4 game development excellent medium \n", "... ... ... ... \n", "6896 hacking poor poor \n", "6897 hacking excellent excellent \n", "6898 data science poor poor \n", "6899 game development poor excellent \n", "6900 database security excellent medium \n", "\n", " Interested subjects interested career area \\\n", "0 programming testing \n", "1 Management system developer \n", "2 data engineering Business process analyst \n", "3 networks testing \n", "4 Software Engineering system developer \n", "... ... ... \n", "6896 Software Engineering testing \n", "6897 programming testing \n", "6898 IOT system developer \n", "6899 data engineering developer \n", "6900 Computer Architecture security \n", "\n", " Type of company want to settle in? Taken inputs from seniors or elders \\\n", "0 BPA no \n", "1 Cloud Services yes \n", "2 product development yes \n", "3 Testing and Maintainance Services yes \n", "4 BPA no \n", "... ... ... \n", "6896 Testing and Maintainance Services yes \n", "6897 Testing and Maintainance Services no \n", "6898 Cloud Services yes \n", "6899 SAaS services no \n", "6900 Sales and Marketing yes \n", "\n", " Interested Type of Books Management or Technical hard/smart worker \\\n", "0 Series Management smart worker \n", "1 Autobiographies Technical hard worker \n", "2 Travel Technical smart worker \n", "3 Guide Management smart worker \n", "4 Health Technical hard worker \n", "... ... ... ... \n", "6896 Trilogy Management smart worker \n", "6897 Science Management hard worker \n", "6898 Self help Technical hard worker \n", "6899 Drama Technical smart worker \n", "6900 Drama Management smart worker \n", "\n", " worked in teams ever? Introvert Suggested Job Role \n", "0 yes no Applications Developer \n", "1 no yes Applications Developer \n", "2 no no Applications Developer \n", "3 yes yes Applications Developer \n", "4 yes no Applications Developer \n", "... ... ... ... \n", "6896 no yes Web Developer \n", "6897 no no Web Developer \n", "6898 yes no Web Developer \n", "6899 no yes Web Developer \n", "6900 yes no Web Developer \n", "\n", "[6901 rows x 16 columns]\n" ] } ], "source": [ "# Now we understand there are 4 numerical values, 16 categorical values (nominal or binary[Yes/No])\n", "# Therefore, we need to split encoding as each of the question is different with answer option \n", "# For example, there are about 12 options (will be encoded 0-11) in suggested job role vs yes/no question that will be only (0/1)\n", "# Hence, we will proceed with Binary Encoding, then nominal encoding (Ordinal/ Label encoding)\n", "# Call Categorical variables for referring and columns name to copy and paste:\n", "print(categorical_cols.columns.tolist())\n", "print(categorical_cols)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "bicols = data[['self-learning capability?', 'Extra-courses did', 'Taken inputs from seniors or elders','worked in teams ever?', 'Introvert']]\n", "for i in bicols:\n", " replace_nums = {i: {\"yes\": 1 ,\"no\": 0}}\n", " data = data.replace(replace_nums )\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 6901 entries, 0 to 6900\n", "Data columns (total 20 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Logical quotient rating 6901 non-null int64 \n", " 1 hackathons 6901 non-null int64 \n", " 2 coding skills rating 6901 non-null int64 \n", " 3 public speaking points 6901 non-null int64 \n", " 4 self-learning capability? 6901 non-null int64 \n", " 5 Extra-courses did 6901 non-null int64 \n", " 6 certifications 6901 non-null object\n", " 7 workshops 6901 non-null object\n", " 8 reading and writing skills 6901 non-null int64 \n", " 9 memory capability score 6901 non-null int64 \n", " 10 Interested subjects 6901 non-null object\n", " 11 interested career area 6901 non-null object\n", " 12 Type of company want to settle in? 6901 non-null object\n", " 13 Taken inputs from seniors or elders 6901 non-null int64 \n", " 14 Interested Type of Books 6901 non-null object\n", " 15 Management or Technical 6901 non-null object\n", " 16 hard/smart worker 6901 non-null object\n", " 17 worked in teams ever? 6901 non-null int64 \n", " 18 Introvert 6901 non-null int64 \n", " 19 Suggested Job Role 6901 non-null object\n", "dtypes: int64(11), object(9)\n", "memory usage: 1.1+ MB\n", "None\n" ] } ], "source": [ "orcols = data[[\"reading and writing skills\", \"memory capability score\"]]\n", "for i in orcols:\n", " replace_nums = {i: {\"poor\": 0, \"medium\": 1, \"excellent\": 2}}\n", " data = data.replace(replace_nums)\n", "\n", "print(data.info())\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Logical quotient ratinghackathonscoding skills ratingpublic speaking pointsself-learning capability?Extra-courses didcertificationsworkshopsreading and writing skillsmemory capability score...Type of company want to settle in?Taken inputs from seniors or eldersInterested Type of Booksworked in teams ever?IntrovertSuggested Job RoleManagement or Technical_ManagementManagement or Technical_Technicalhard/smart worker_hard workerhard/smart worker_smart worker
05062104600...002810Applications DeveloperTrueFalseFalseTrue
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32635017220...711311Applications DeveloperTrueFalseFalseTrue
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" ], "text/plain": [ " Logical quotient rating hackathons coding skills rating \\\n", "0 5 0 6 \n", "1 7 6 4 \n", "2 2 3 9 \n", "3 2 6 3 \n", "4 2 0 3 \n", "\n", " public speaking points self-learning capability? Extra-courses did \\\n", "0 2 1 0 \n", "1 3 0 1 \n", "2 1 0 1 \n", "3 5 0 1 \n", "4 4 1 0 \n", "\n", " certifications workshops reading and writing skills \\\n", "0 4 6 0 \n", "1 8 6 2 \n", "2 4 6 2 \n", "3 7 2 2 \n", "4 1 3 2 \n", "\n", " memory capability score ... Type of company want to settle in? \\\n", "0 0 ... 0 \n", "1 1 ... 1 \n", "2 0 ... 9 \n", "3 0 ... 7 \n", "4 1 ... 0 \n", "\n", " Taken inputs from seniors or elders Interested Type of Books \\\n", "0 0 28 \n", "1 1 3 \n", "2 1 29 \n", "3 1 13 \n", "4 0 14 \n", "\n", " worked in teams ever? Introvert Suggested Job Role \\\n", "0 1 0 Applications Developer \n", "1 0 1 Applications Developer \n", "2 0 0 Applications Developer \n", "3 1 1 Applications Developer \n", "4 1 0 Applications Developer \n", "\n", " Management or Technical_Management Management or Technical_Technical \\\n", "0 True False \n", "1 False True \n", "2 False True \n", "3 True False \n", "4 False True \n", "\n", " hard/smart worker_hard worker hard/smart worker_smart worker \n", "0 False True \n", "1 True False \n", "2 False True \n", "3 False True \n", "4 True False \n", "\n", "[5 rows x 22 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.select_dtypes(include=\"object\").columns.tolist()\n", "nominalcols = ['certifications',\n", " 'workshops',\n", " 'Interested subjects',\n", " 'interested career area ',\n", " 'Type of company want to settle in?',\n", " 'Interested Type of Books']\n", "#Left with these nominal attributes\n", "data.select_dtypes(include=\"object\").head()\n", "for i in nominalcols:\n", " data[i] = data[i].astype('category')\n", " data[i] = data[i].cat.codes\n", "\n", "data = pd.get_dummies(data, columns = ['Management or Technical','hard/smart worker'])\n", "data.head()\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "#done converting all to numerical left with the label attribute\n", "#try with building machine learning model\n", "dataset = data\n", "dataset.head()\n", "#Separate all independant variables and targeted variable column\n", "df_train_x = dataset.drop('Suggested Job Role', axis = 1)\n", "df_train_y = dataset['Suggested Job Role']\n", "#Therefore, we split the dataset into train and test dataset\n", "x_train, x_test, y_train, y_test = train_test_split(df_train_x,df_train_y,test_size=.20,random_state=42)\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 8 14 10 6 15 4 11 16 11 9 13 15]\n", " [ 9 14 13 9 10 8 10 6 4 5 9 8]\n", " [ 8 11 14 9 13 9 10 8 11 12 8 10]\n", " [ 7 5 7 9 11 7 9 8 10 6 10 11]\n", " [12 13 6 7 18 11 8 11 9 8 9 9]\n", " [ 7 13 9 6 14 12 9 13 9 8 4 9]\n", " [ 4 10 14 9 15 13 10 9 6 8 11 7]\n", " [ 7 11 15 10 12 13 6 10 10 11 7 6]\n", " [ 5 11 11 5 23 12 9 3 7 9 12 7]\n", " [ 5 8 8 7 14 15 8 10 13 6 6 11]\n", " [10 16 11 4 14 16 10 5 14 8 5 6]\n", " [ 7 8 10 8 10 16 12 12 6 8 5 7]]\n", "\n", "0.8689355539464156\n" ] } ], "source": [ "#Proceed with random forest classifier\n", "rf = RandomForestClassifier(random_state=10)\n", "rf.fit(x_train,y_train)\n", "rf_predict_y = rf.predict(x_test)\n", "rfc_cm = confusion_matrix(y_test,rf_predict_y)\n", "rfc_acc = accuracy_score(y_test,rf_predict_y)\n", "print(rfc_cm, end=\"\\n\\n\")\n", "print(rfc_acc*10)\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "userdata = [['7','6','6','8','3','5','4', '4', '7', '3', '3', '6','8', \n", " '7','5','7','4','5','6','8','8']]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Software Engineer']\n", "Probabilities of all classes: [[0.07 0.11 0.07 0.05 0.05 0.06 0.12 0.11 0.11 0.09 0.06 0.1 ]]\n", "Probability of Predicted class : 0.12\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\Asus\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:465: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n", " warnings.warn(\n", "c:\\Users\\Asus\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:465: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n", " warnings.warn(\n" ] } ], "source": [ "ynewclass = rf.predict(userdata)\n", "ynew = rf.predict_proba(userdata)\n", "print(ynewclass)\n", "print(\"Probabilities of all classes: \", ynew)\n", "print(\"Probability of Predicted class : \", np.max(ynew))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pickle \n", "pickle.dump(rf, open('rfweights.pkl','rb'))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.2" } }, "nbformat": 4, "nbformat_minor": 2 }