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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.io import loadmat\n",

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding highest amount of diamonds\n",
    "## Job interview exercise for Medecide"
   ]
  },

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automobile prices regression\n",
    "## hello all\n",
    "This is a simple random forest classifier for regression of automobile prices, the dataset is located <a href=\"https://archive.ics.uci.edu/ml/datasets/automobile\">here</a>"
   ]

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imubit\n",
    "This is my solution for the challenge. I will explain my steps through this notebook. "
   ]
  },

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imubit\n",
    "This is my solution for the challenge. I will explain my steps through this notebook. "
   ]
  },

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regularization\n",
    "\n",
    "Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that **overfitting can be a serious problem**, if the training dataset is not big enough. Sure it does well on the training set, but the learned network **doesn't generalize to new examples** that it has never seen!\n",
    "\n",