added project1 files
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bb8c7a0491
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decision_tree.joblib
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decision_tree.joblib
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diamonds.csv
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diamonds.csv
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multiple_regression.joblib
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multiple_regression.joblib
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project_1_eda_and_model_training.ipynb
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project_1_eda_and_model_training.ipynb
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project_1_predictions.ipynb
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project_1_predictions.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "11e53f32-8614-4020-9fce-b838ad409098",
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"metadata": {
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"id": "HPsfw4s7kiKk",
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"tags": []
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn import metrics\n",
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"from joblib import dump, load # sklearn's replacement for pickle"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "73cb34f0-0771-4df5-bb29-afee3d2f6350",
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"metadata": {},
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"outputs": [],
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"source": [
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"# load saved models\n",
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"model_1 = load('simple_regression.joblib')\n",
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"model_2 = load('multiple_regression.joblib')\n",
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"model_3 = load('decision_tree.joblib')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3023a4a8-7deb-4370-8ad6-8ab61bba514f",
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"metadata": {},
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"source": [
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"## Predictions: model 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "b424b756-c488-426b-95d1-5757ab2dd413",
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"metadata": {},
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"outputs": [],
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"source": [
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"test_x = np.zeros((1, 1)) # create an empty vector for 'carat'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "851ccc74-7cdf-43af-aed1-d3f3777768a9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[0.7]]\n"
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]
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}
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],
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"source": [
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"test_x[0, 0] = 0.70 # weight of the diamond\n",
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"print(test_x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "04fc4367-9022-49b5-a5e6-5721c0671f92",
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"metadata": {},
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"outputs": [],
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"source": [
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"test_y = np.zeros((1, 1)) # create an empty vector for 'price'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "17d43990-28a3-4799-b10e-68373208dc66",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[2751.]]\n"
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]
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}
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],
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"source": [
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"test_y[0, 0] = 2751.00 # price of the diamond\n",
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"print(test_y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "5eedcc23-376a-4fc4-b4a5-9a20c4674b79",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2996.33847049]\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/tim/Nextcloud/Documents/predictive_analytics/envs/lib/python3.10/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"prediction = model_1.predict(test_x)\n",
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"print(prediction)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "29a7b8ac-31cf-4193-93e6-cd81417a635e",
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"metadata": {
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"id": "cQEsaET7Oj8o"
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},
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"outputs": [],
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"source": [
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"mse = metrics.mean_squared_error(test_y, prediction)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "86a0249c-7987-4510-994a-e4add582c0c5",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"245.338470494803"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.sqrt(mse) # root mean squared error"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f7821a20-26bd-4703-ad09-0224aa331c07",
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"metadata": {},
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"source": [
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"## Predictions: model 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "802e6f37-b0e1-403d-865e-489d5b85f53f",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "YVeMEFuEn4wi",
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"outputId": "bbeb9369-e01a-4358-f517-53d57f238b64"
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},
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"outputs": [],
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"source": [
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"test_x = np.zeros((1, 6)) # create an empty array for the features"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "900cfbc7-ea3e-4f55-97e3-4b6a9f2b0cbb",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 0.7 4. 4. 4. 62. 55. ]]\n"
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]
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}
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],
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"source": [
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"test_x[0, 0] = 0.70\n",
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"test_x[0, 1] = 4.00\n",
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"test_x[0, 2] = 4.00\n",
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"test_x[0, 3] = 4.00\n",
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"test_x[0, 4] = 62.00\n",
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"test_x[0, 5] = 55.00\n",
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"print(test_x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "3965a843-809f-492b-9f79-c6577b062c76",
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"metadata": {},
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"outputs": [],
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"source": [
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"scaler = StandardScaler() # instantiate the scaler"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "d70c4f75-e644-4a4a-b546-d0eed460f76a",
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"metadata": {},
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"outputs": [],
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"source": [
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"scaled_X = scaler.fit_transform(test_x) # scale the array"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "7ac2f206-e400-4ac7-be27-1250f48b2d9c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[2751.]]\n"
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]
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}
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],
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"source": [
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"# y remains the same\n",
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"print(test_y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "53b6f21f-caa9-4d6f-b90b-2b49c00879cd",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2572.08756099]\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/tim/Nextcloud/Documents/predictive_analytics/envs/lib/python3.10/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"prediction = model_2.predict(scaled_X)\n",
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"print(prediction)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "855b89da-82e0-4b67-8ad7-0bcffd9bf2d5",
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"metadata": {
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"id": "cQEsaET7Oj8o"
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},
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"outputs": [],
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"source": [
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"mse = metrics.mean_squared_error(test_y, prediction)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "ef1098b5-3941-4df0-b427-d0089736a65c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"178.91243900831523"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.sqrt(mse) # root mean squared error"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "26996ee9-3b6e-4e37-9f90-7a2fb8c0979d",
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"metadata": {},
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"outputs": [],
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"source": [
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"## Predictions: model 3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "2cf9462e-d339-4f0b-bcc5-bacc31890374",
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"metadata": {},
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"outputs": [],
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"source": [
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"# test_x remains the same\n",
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"# scaled_X remains the same\n",
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"# test_y remains the same"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "be587251-63a5-4e1e-8b95-7da381414699",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1667.]\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/tim/Nextcloud/Documents/predictive_analytics/envs/lib/python3.10/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"prediction = model_3.predict(scaled_X)\n",
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"print(prediction)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "753d5621-78ff-4be8-9e11-f74473ba48b6",
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"metadata": {
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"id": "cQEsaET7Oj8o"
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},
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"outputs": [],
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"source": [
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"mse = metrics.mean_squared_error(test_y, prediction)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"id": "e50e4073-8da9-4ca2-9551-eedbe9dbf631",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1084.0"
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]
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},
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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.sqrt(mse) # root mean squared error"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "e76d5e9b-1ac9-477a-9dbc-c30a5df50c4a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Model 1 returned the smallest RMSE, followed by model 2, then model 3"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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simple_regression.joblib
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simple_regression.joblib
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